Built an AI tools directory, an SERP preview tool, an Responsive Search Ad preview tool, a landing page template, a portfolio site and a lot more.

All using just prompts.

Tools that used to need dev resources, dependency management, responsiveness fixes, and three sprint cycles are now a few prompts away from going live.

ROI calculators. Portfolio websites. Subject line testers. Custom reports for ABM.

None of these belong in a sprint anymore. They belong in a Friday afternoon.

The vibe coding process is genuinely fun.

You describe what you want, throw in a screenshot, and watch it come to life. For someone with no dev background, that feeling does not get old.

Dictating prompts instead of typing them is low-key one of the best things about the whole experience.

But here is the catch. Credits run out fast. Way faster than you think.

And before you know it, you have burned through half your monthly balance and you are not even done with one tool.

The good news: most of it is avoidable. Here are 8 tips that actually help.

1. Start with plan mode. Save time. Save credits.

Before Lovable writes a single line of code, Plan Mode shows you exactly what it is about to build. You can review the structure, push back on it, refine it, and only approve it when it looks right.

Plan Mode forces a back and forth between you and Lovable about what the project actually is before anything gets built. That conversation upfront saves a lot of confusion later. The more clearly Lovable understands what you want, the less it improvises.

Think of it as the 5 minutes that saves you 50.

Here’s a sample of a brief:

Project: AI Tools Directory for B2B Marketers

What to build: A searchable, filterable directory of AI tools for B2B marketers. Each tool card shows the tool name, logo/icon, category badge, and a short description. Users can browse all tools, filter by category (e.g. Productivity, AI Writing & Copy, SEO/AEO/GEO, Lead Database, Signal Intelligence, Outbound Email, Email Deliverability, LinkedIn Automation, ABM, Video & Podcast, Analytics), and search by keyword.

Brand guidelines: Dark theme. Inter font. White for primary text, Orange for secondary.

2. Set up Knowledge

If you already have a sense of your brand voice, ICP, fonts, colour schemes, or design style, set those up in Knowledge before you start building. You can do this at the project level for tool-specific instructions, or at the workspace level if these guidelines apply across everything you build.

3. Switch to Chat Mode before debugging

Chat Mode does not touch your code. It costs one credit flat.

Use it to diagnose before you fix. Ask Lovable to analyse the error, identify the root cause, and explain what is happening. Then switch to build mode with a clear picture of what is actually broken.

Blind fixes are the most expensive thing you can do in Lovable. You prompt, it tries something, it breaks something else, you prompt again. The balance drains fast.

3. Off-Platform Brainstorming

Credits burn when you use Lovable as a whiteboard. Every message you send to figure out what you want to build is a credit spent on thinking, not building. Draft your instructions in Claude or ChatGPT first. Iterate there for free. Refine until the prompt is clear, specific, and ready. Then paste the finalised version into Lovable.

When I was building the AI Tools Directory for B2B marketers, I did not add tool descriptions one by one. That would have taken forever and cost a fortune in credits.

Instead I used Claude to generate a full repository of tools with all the relevant fields. Spent some time reviewing and refining it. Then handed the file to Lovable. In a matter of minutes, the entire directory was populated.

It even used the Clearbit API to pull in logos automatically. Some were off and I had to upload those manually, but honestly, uploading one Excel file and watching hundreds of tool names, descriptions, logos, and links populate in minutes is nothing less than magic.

4. Batch your prompts

Lovable has a prompt queue. While it is working on one instruction, you can keep stacking the next ones. You can reorder them, pause the queue, and come back to meaningful progress instead of watching a spinner.

Batch the work. Let it run.

5. Use the Visual Editor for cosmetic tweaks

Lovable has a visual editor that works like a lightweight Figma. Click any element in the live preview and adjust spacing, colour, or sizing directly. Changes appear instantly. No prompt needed.

Use the visual editor for anything cosmetic. Button colours, padding, font sizes, layout spacing. Click it, fix it, move on. You can even hold Cmd and select multiple elements.

You save credits. And you avoid breaking things you never intended to touch.

6. Try to Fix doesn't cost credits

Good news: when Lovable flags an error, the “Try to Fix” button does not count against your credits.

Use it every time. Give it up to three attempts on the same error before you write a new prompt. It fixes more than you expect.

If it is still not resolved, here is a simple flow that works. Copy the error. Paste it into Chat Mode. Ask Lovable to identify the root cause. Once you understand what is actually broken, switch back to build mode and fix it.

8. Build mobile first from the start

This is not just a design tip. It is a credit tip.

Build everything on desktop first and mobile responsiveness becomes a problem you fix at the end. That fix costs credits. A lot of them.

Tell Lovable to build mobile-first from your very first prompt.
One instruction at the start saves you many later.

If you only want to make changes on the mobile version without touching the desktop layout, you can do that too.

A few years ago, building a live marketing tool meant a dev ticket, a sprint, a review, and a wait. Now it means a weekend and some prompts.

That shift is genuinely hard to put into words until you feel it yourself.

Happy vibing.

Yours promptly,
Manu

I keep sprinting between Google Meet, Zoom, MS Teams, Slack huddles.

No warm up. No cool down. Just vibes and calendar invites.

At some point, the tools started showing cracks.
But more importantly, I hit what I call THE BOT PROBLEM.

You know the moment.

“AI Notetaker has joined the meeting.”

Instant vibe shift.

Suddenly everyone is slightly more cautious. Slightly more formal. Jokes get filtered. Opinions get polished. You're hyper aware that everything is being recorded.

Yes, documentation improves. But the room changes.
That tradeoff started feeling heavy.

I had heard of Granola before, but didn't fully experiment with it because the other tools were doing… fine. Then I actually tried it. And it clicked.

It runs locally as a native app and transcribes system audio quietly in the background. No visible bot. No awkward energy shift. No one sitting in the corner of every call.

Once the meeting ends, it turns the transcript into a structured summary.

Here are 10 reasons I made the switch:

1. It doesn't join your meetings.
   Isn't that a big win already? No participant tile. No announcement. No vibe distortion.
   

2. It’s platform agnostic (most importantly works on Slack and in-person meetings)
   It doesn't care where the conversation happens. It just works. For someone juggling platforms all day, that flexibility matters.

   

3. It works on mobile.
   Sometimes it's a hallway chat and it still gets captured. Still well organized.

   

4. I can ask anything about any past meeting.
   Global search and chat across your entire meeting history.
   And the new MCP support with tools like Claude and ChatGPT can directly pull that context without you copy-pasting transcripts around. Open Claude, ask it to draft a follow-up from your last call, and it already knows what happened.

   

5. Action item extraction.

   It automatically scans conversations and builds a checklist and even attempts to No more scrolling through notes trying to decode who volunteered for what.
   

6. Meeting templates.

   One-on-ones. Sales calls. Retros. Kickoffs. Each meeting type gets its own structure. Your notes stop being messy memory dumps.

   

7. Recipes.

   There are pre built AI shortcuts you trigger with a simple slash command.
   Type /what did I miss and it summarizes the last chunk instantly.
   Need a follow-up email? Trigger it.
   It’s like having micro automation inside conversations.

8. Multilingual support.

   From what I’ve seen, it handles Hindi surprisingly well. In Indian work contexts where languages naturally blend mid-conversation, this is genuinely useful.
   

9. Direct integrations.

   Push notes to Notion, Slack, Google Docs, CRMs.
   No manual copy-paste gymnastics.
   

10. Saves time.

    The summary is ready before you've closed the tab. Action items are already extracted. The follow-up email is one recipe trigger away. Over a month, that compounds into a meaningful amount of reclaimed time. Just hours back.

The one limitation worth mentioning.

Granola doesn’t record video or capture screen recordings. If you’re in a call where someone’s walking through a deck, a live demo, or a complex diagram, you get the words, not the visuals.

But it’s a trade-off I’ll take every time.

Everything else? Works out perfect.

The admin tax of 3 to 5 meetings a day is brutal.

Bots fix note taking well.

But they mess with the human layer of conversation.

Granola feels like the sweet spot.

Fully present in the room.
Perfect notes afterward.
No ro(bot) sitting in the corner of every call.

And once you taste that level of leverage, it’s hard to go back.

Yours Promptly,
Manu

• Free users: See ads, can opt out by accepting message limits

• Go users ($8/month): See ads, no opt-out option

• Plus/Pro/Business/Enterprise/Education ($20+/month): No ads

The rollout is gradual, so not everyone sees ads yet even if they're on a free or Go plan.

But here's where it gets interesting.

This isn't just about sponsored ads on a chat interface.

OpenAI has been building great ad infra:

• Atlas Browser gives them access to your entire web activity

• App integrations enables cross-platform data collection

• Partnership with Stripe means instant in-chat purchases

Plus, the Meta-fication of OpenAI is real with 20% of OpenAI staff being ex-Meta.

What This Means For Marketers

This might be the biggest acquisition shift.

Why? Intent.

Traditional search ads target keywords. Social ads target demographics and interests. ChatGPT ads will target something far more valuable: actual problems people are actively trying to solve, expressed in their own words, with full context.

Neil Patel outlines in his breakdown, here’s what ChatGPT ads are likely to look like:

Sponsored Responses: Paid recommendations placed directly inside the answer. The brand becomes the first suggested solution for high-intent queries.

Native Product Mentions: Tools or services woven naturally into the response. No banners, just suggestions that feel organic.

Contextual Recommendations: Products suggested based on the flow of the conversation. Triggered by what the user is actively discussing, not just keywords.

Intent-Based Targeting: Targeting built on real problems expressed in real language. It understands context, constraints, and buying intent in the moment.

The opportunity for early movers is massive. But that arbitrage window won’t last long. Once everyone figures this out, costs will normalize.

Meanwhile, At The Super Bowl...

While OpenAI was adding sponsored toppings to the dish, Anthropic was talking about farm-to-table trust.

Anthropic decided to take a public stand. During the Super Bowl (yes, THE Super Bowl), they ran a campaign mocking the entire concept of ads in AI conversations.

Ad 1: Is my essay making a clear argument
A student is writing an essay and the AI assistant tells him it’s fresh and well-thought-out, but then recommends jewellery because his deadline is today.

Ad 2: What do you think of my business idea?
A woman is asking for advice on her new business idea and the AI assistant tells her it’s creative and suggests a step-by-step plan, but then recommends payday loans.

Ad 3: How can I communicate better with my mom?
A young woman asks for advice on communicating with her mom and the AI assistant suggests listening and shared activities, but then recommends a dating site.

Ad 4: Can I get a six pack quickly?
A young man asks for help getting a six-pack and the AI assistant suggests a personalized workout plan but then recommends height-increasing insoles.

The entire campaign positioned Claude as the premium, privacy-focused alternative at the exact moment OpenAI was testing ads. Whether this is a sustainable business model or just excellent tactical marketing remains to be seen.

This week: Anthropic - 1. ChatGPT - 0.

But the scoreboard isn’t the story.

ChatGPT has your saved memories and your entire chat history. It knows what you're working on, what you're worried about, what you're shopping for, what problems you're trying to solve.

This isn't targeting based on what you clicked last week. This is targeting based on what you told your AI assistant during a vulnerable 2am conversation.

It’s user profiling at scale.

For 95% of ChatGPT's 800 million weekly users, the trade-off is:
See ads or pay $20/month.

Some might be okay. Some might upgrade. Some might not even notice when a recommendation is genuinely best versus paid.

We'll see how this plays out.

For now,
if you're on the free tier, expect sponsored links.
If you're a marketer, stay tuned for the opportunity.
And if you're from Anthropic, well done and congrats :)

Yours Promptly,
Manu

The blog that that starts with “In today’s fast-paced landscape...”
Too many Em-dashes on LinkedIn post.

It shows a lack of effort. But it's still identifiable. You can still tell.

That window is closing.

As tools get cheaper, faster, and better at mimicking human voice (coughs ElevenLabs) and video (coughs again HeyGen), the gap between real and generated shrinks to nothing.

The tells disappear.

The structure gets varied. The tone gets trained. The slop gets smarter.

In a few months, you won’t be able to spot AI slop by seeing it. You’ll just not know.

And that’s when the real problem starts.

The Real Crisis Isn't Quality. It's Trust.

When you can't tell the difference between human-crafted and AI-generated, you stop trusting either.

This is already happened in the past.

People don't read reviews the same way anymore.
They assume the 5 star ratings are gamed.

They've learned that polished equals suspicious.

Trust is becoming the scarcest resource.

Here's What May Happen Next

When everything looks the same, the different becomes valuable.

It happened with watches.
Smartwatches went mainstream and suddenly analog had a revival.

Digital cameras got too clean and everyone started craving Polaroids.
Fast fashion made every closet look identical and handcrafted became the premium signal.

If history repeats, and it usually does, AI content will become the new fast fashion. Everywhere. Indistinguishable. And quietly ignored.

The question isn’t whether trust becomes scarce.
It’s who’s building it before everyone else figures that out.

Three places where it’s already happening.

1. Founder brand and employee brand:
Organic social is becoming the company's most valuable marketing asset. HubSpot built a media machine around its people. Lovable is growing on the back of its unfiltered building-in-public content.

Buyers are watching, reading, and forming opinions long before a sales conversation ever starts. Our job here is to find the genuine story, the real conviction, the opinions that might make some people uncomfortable, and get it out consistently.

Over-engineered posts get ignored. Real ones get shared.

2. Experiential events:
Dinners, roundtables, curated gatherings where the organizer has thought hard about who belongs in the room together.

The magic is simple: you cannot fake presence. An hour at the right dinner does more trust-building than a 60 page whitepaper. Marketers who create and own these moments, rather than just sponsoring someone else's.

3. Communities:
The most trusted conversations in B2B are happening in places no brand has access to. Independent WhatsApp groups. Private Slack communities. Discord servers where practitioners talk to each other without a company moderating the thread.

You can’t buy your way in. You can’t automate your way in. The only way in is to be good enough that someone else brings you up.

Which is exactly why it matters.

Welcome to Trust Economy

While everyone is scrambling to keep up with the latest tools and AI updates, the real game is about building something that can’t be replicated by anyone with a good prompt. That means making the case internally for slower work.

Founder brand takes months. Good events take planning and real relationships.

None of it shows up in the short-term dashboard.
All of it compounds in ways that become very hard to compete with.

And the window to build it is smaller than we all think.

Yours Promptly,
Manu

Research docs. Customer calls. Competitive pages. Earnings notes. Slack threads. PDFs that felt important once and now live in Untitled folder.

The job is no longer to create more content but to extract signal from noise.

That's what Google NotebookLM is built for.
You bring your sources, it works only from those.

And because it's Google, it connects to the places where your knowledge already lives. Drive, YouTube, Gmail, Search. Your docs, your research, your calls.

All of it queryable in one place.

Which means NotebookLM isn’t trying to guess context from the internet. It’s built to sit right inside the places where your real knowledge already lives.

That one constraint is massive for GTM work.

In this letter, you'll learn how to get started with NotebookLM, walk through each feature in detail, and see exactly how to put it to work

First, open NotebookLM. Hit Create Notebook in the top right. Give it a name.

Next, you add sources.

Let’s say you want to get up to speed on AEO.
You create a notebook, name it Answer Engine Optimization, and start adding sources.

You can search the Web manually and pick what looks relevant. Or you can use Fast Research, type in Answer Engine Optimization, and let NotebookLM pull the most useful sources for you. Articles, YouTube videos, guides. You review, select, and add.

You can also bring in sources you already have.
Paste the link or upload the file and it’s in.

That’s it. Your notebook is ready to work with.

Once your sources are in, you can start asking questions directly in the chat.

NotebookLM answers from what it read. And every answer comes with citations. Click on any claim and it takes you back to the exact source and passage it pulled from. You can verify, challenge, or dig deeper.

That’s the part that matters for GTM work. You’re not guessing whether the answer is accurate. You can see exactly where it came from.

On the right, you have the Studio panel.

This is where you turn your sources into something useful. Audio overviews, slide decks, video overviews, mind maps, reports, flashcards, quizzes, infographics, and data tables.

Let’s get into each one.

1. Reports

NotebookLM suggests formats based on what it reads: briefing docs, study guides, blog posts. Or you can pick from its own suggestions, things like a strategic white paper, an implementation roadmap, or give it custom instructions if you want something specific.

Pick strategic white paper, hit generate, and it returns a pretty decent report based entirely on your sources.

Let’s pick strategic white paper, hit generate, and it returns a pretty decent report based entirely on your sources.

💡: There's Add Note option at the bottom of the Studio panel where you can add your own notes, observations, or a hypothesis and NotebookLM will treat it as a source alongside everything else.

2. Slide Deck

NotebookLM builds a full presentation from your sources. You choose between a visual deck or a text-heavy one depending on what you need.

You can also revise individual slides with prompts and export as PDF or PowerPoint when you're done.

3. Audio Overview

NotebookLM can turn your sources into a podcast-style summary.
Then listen on the NotebookLM mobile app. This super is underrated.
Honestly, the output is pretty impressive.

How to rank in AI answers

You can also generate the audio in different languages, which is pretty cool.

4. Video Overview

NotebookLM turns your sources into a short narrated video. You pick the format: an explainer, a brief, or a deep dive. Then choose a visual style, whiteboard, cinematic, or slideshow depending on how polished you need it to look.

The output generated for AEO Explainer was pretty solid.

Explainer_AEO Video:

5. Mind Map

NotebookLM takes your sources and builds a visual map of the key concepts and how they connect. It’s interactive: click on any node and it opens a focused chat on that specific subtopic.

Useful when you’re trying to map out a content strategy, understand a new market, or pressure-test your positioning before a launch. Sometimes seeing the relationships between ideas is faster than reading about them.

6. Infographics

NotebookLM turns your sources into a single visual summary. It’s quite useful for turning a research finding or competitive landscape into a shareable asset without briefing a designer.

7. Flash Cards

NotebookLM turns your sources into active recall cards designed for spaced repetition.

8. Quiz

NotebookLM creates multiple choice questions from your sources with adjustable difficulty levels. Same territory as flashcards. Most useful for onboarding and enablement. Upload your product docs and battlecards, generate a quiz, and send it to new SDRs before their first training call.

9. Data Table

NotebookLM pulls scattered information from your sources and organizes it into a structured, sortable comparison table. You can export it directly to Google Sheets.

The features are the toolkit. Here are six marketing plays use cases where NotebookLM does work that would otherwise take hours.

1. Competitive Analysis

Upload your competitor websites, landing pages, and blog posts. Ask it to generate a comparison table across positioning, audience, messaging, and tone. Then push deeper: what problem is each competitor leading with, what emotion are they triggering, and what are they trying to own?

You get a structured, cited analysis you can share with your team, feed into a campaign brief, or use to build a battlecard. In a fraction of the time.

Here is a sample prompt:

You are a senior competitive intelligence analyst. I have uploaded a set of competitor websites. Your job is to conduct a thorough competitive analysis across all sources.

Do the following in this order:

First, generate a structured comparison table with these columns: company name, core value proposition, primary target audience, key messaging themes, tone of voice, content strategy, and primary call to action. Cite the source for each claim.

Second, go deeper on each competitor. For each one answer: what problem are they leading with, what emotion are they trying to trigger in their buyer, and what is the one thing they are clearly trying to own in the market.

Third, identify five positioning gaps across the full competitive set. A gap is an angle, an audience segment, or a pain point that no competitor is clearly owning. For each gap explain why it exists and why it is an opportunity.

Fourth, write a one paragraph positioning recommendation based on what you found. Where should we play that no one else is playing?

Format the output as: Comparison Table, Competitor Deep Dives, Positioning Gaps, Recommendation. Use headers for each section. Keep language sharp and direct.

2. Market Trend Analysis

Upload an industry PDFs and ask NotebookLM to pull out the top emerging trends with one actionable recommendation per trend. But don’t stop at summaries. Ask it to act as a strategic partner and surface the questions a marketing leader should actually be asking about the research. Then ask for a gap analysis to flag what’s missing from your sources.

Here is a sample prompt:

You are a strategic marketing analyst. I have uploaded a set of industry reports and research documents. Your job is to turn this into something a marketing team can act on, not just read.

Do the following in this order:

First, identify the top five emerging trends most relevant to marketers based strictly on the uploaded sources. For each trend provide: a two to three sentence plain-language summary, the specific evidence from the sources that supports it with citations, one marketing implication explaining what this means for how we go to market, and one concrete action our team should take in the next 90 days.

Second, propose five critical questions a marketing leader should be asking based on what this research reveals. Not generic questions. Questions that only make sense given what is in these specific documents.

Third, flag what is missing. What topics, data points, or perspectives are absent from these sources that we should find before making any strategic decisions?

Output format: Trends with a sub-section per trend, then Critical Questions as a numbered list, then Gaps as a short paragraph. Write it so it can go directly into a leadership meeting.

3. Voice of Customer

Upload 20 to 30 raw customer reviews from YouTube, call recordings, customer testimonials, or wherever your buyers leave honest feedback. Ask NotebookLM to identify the top 5 pain points customers had before finding your product, and extract 10 direct quotes that highlight a specific benefit or outcome.

Here is a sample prompt:

You are a conversion customer research analyst. Your job is to mine raw customer language and turn it into marketing intelligence that goes directly into copy, positioning, and campaign briefs.

I have uploaded a set of customer reviews, testimonials, survey responses, and or interview transcripts. Do not paraphrase loosely. Stay close to what customers actually said.

Do the following in this exact order:

First, identify the 5 five pain points customers experienced before finding this product. For each pain point write a one sentence summary of the core frustration and rate how frequently this theme appears across the sources as high, medium, or low.

Second, extract customer quotes that describe a specific benefit, transformation, or measurable outcome. For each quote add: a one sentence note on where this could be used in marketing such as homepage headline, ad copy, sales email, or objection handling, and a suggested rewrite of the quote that keeps the customer’s voice but tightens it for use in copy.

Third, build a language map. List the exact words and phrases customers use repeatedly to describe their problem, their desired outcome, and the product itself. These are the words our copy should use, not the words we think sound good. Organise into three columns: problem language, outcome language, product language.

Fourth, identify every objection, hesitation, or frustration that appears more than once across the sources. For each one write a one sentence marketing response that directly addresses it, suitable for use in a landing page FAQ or sales email.

Output format: Pain Points Table, Quotes Library, Language Map, Objection Handler. Present as clean tables throughout.

4. Content Repurposing

Upload a single webinar transcript or video and ask NotebookLM to generate a full week of content from it. One prompt can return a 800-word blog post, a LinkedIn post with audience questions, and 3 email campaigns.

Here is a sample prompt:

You are a content marketing manager who specialises in extracting maximum value from a single piece of long-form content. Your job is to turn 1 webinar into a full week of content across multiple formats, without adding anything that is not in the original source.

I have uploaded a webinar video. Use only what is in this transcript. Do not invent examples, statistics, or claims that are not in the source material.

Generate the following assets in this order:

• A 600 to 800 word blog recap with a compelling opening paragraph that does not start with a summary, 3 clearly structured key takeaways with subheadings that use the speaker’s actual language where relevant, and a closing paragraph with a clear next action for the reader. A FAQ section with 5 questions a buyer would realistically ask after watching this webinar, with concise answers grounded in what was said.

• A 3-email follow-up sequence for attendees. Email 1 sent same day: thank you with the single most important takeaway. Email 2 sent 3 days later: go deeper on 1 specific insight with a soft CTA pointing to a relevant resource or next step. Email 3 sent 7 days later: a direct offer, whether that is a demo, a consultation, a download, or a relevant product page. Write a subject line for each email.

• 1 LinkedIn post between 150 and 200 words. Open with the most surprising insight from the webinar. Build to a key lesson. End with a genuine question for the audience.

• 3 social posts under 280 characters each. Each post leads with a different insight from the transcript. Label them Post 1, Post 2, Post 3.

Label every asset with a clear header. Note the timestamp or speaker quote from the transcript that each major claim is drawn from.

The information problem is not going away. If anything, it gets worse every quarter. More reports, more calls, more content, more noise.

NotebookLM does not solve that problem.
But it gives you a way to work inside it without drowning.

Upload what matters. Ask better questions. Get answers you can actually use.

That is the signal.

Yours Promptly,
Manu

Search engines are exhausted.
Buyers are even more exhausted.

They roll their eyes at vendors insisting their product is the best thing since sliced bread.

And they quietly go looking for answers in places that actually feel alive.
A place where the truth starts to show.
A place that surfaces actual problems, calls out the pretence, and provides the clarity most sites spent years trying to manufacture.

And here is the plot twist most marketers did not see coming.

Reddit.

Reddit is quietly becoming one of the most influential stops in the entire B2B buyer journey. It is becoming a brutally honest corner of the internet for anyone trying to find real information about your product.

There is now clear evidence of deals being influenced by conversations happening at 2 am inside Reddit threads written by people with usernames that sound like video game characters.

Why Reddit Actually Matters for B2B

1. It is eating your organic search

Go Google any bottom-of-funnel keyword in your category right now. Odds are a Reddit thread is sitting above your product page.

This is not a fluke. Reddit signed a data deal with Google in 2024, and the algorithm has been rewarding the platform ever since.

Reddit threads now consistently rank in the top 3 positions for category keywords, comparison queries, and product research terms. The same keywords your SEO team has been grinding on for months.

The part that should bother you most: these are not niche long-tail terms. They are the generic category searches that map directly to pipeline.

Someone typing in the name of a software category and landing on a Reddit thread full of opinions before they ever see your website.

2. It is feeding the LLMs your buyers now trust

A growing share of B2B buyers are starting their research not on Google, but in AI assistants. They type a question into ChatGPT or Perplexity and take the answer seriously.

What most marketers have not clocked yet is where those answers are coming from. LLMs like ChatGPT, Perplexity, and Gemini pull from Reddit in real time through API access.

The discussions happening in subreddits today are training the responses buyers will get tomorrow.

If your brand is mentioned positively and consistently in relevant threads, it shows up in AI-generated shortlists. If it is not mentioned at all, or mentioned negatively, that is what gets surfaced instead. You do not get to write a prompt to fix that.

3. Your buyers are making decisions there

Buyers do not go to Reddit to get excited about a product. They go there to talk themselves out of a bad one.

The research behavior is risk-averse by nature. Someone has already done the initial evaluation. They have seen the demos, read the G2 reviews, sat through the pitch. Now they want the unfiltered version. What do people who actually use this thing say when they are not in a sales conversation?

That is the question Reddit answers. And because the answers come from anonymous peers with no incentive to sugarcoat anything, buyers trust them in a way they simply do not trust vendor content.

If your product is not part of those threads, someone else is filling in the blank. Either a competitor who has figured this out, or an old negative experience that never got addressed.

The Reddit Marketing Playbook

Ross Simmonds outlines a simple 3 phase Reddit Operating System in his video. It is designed to help you engage on the platform without getting banned, and the entire approach revolves around authenticity, research, and adding real value.

Phase 1: Lurk Before You Leap (Months 1–2)

Most marketers skip this phase and pay for it. Reddit communities have their own cultures, unwritten rules, and tolerance for marketing. You need to understand them before you open your mouth.

Find where your audience actually lives: Tools like SparkToro let you cross-reference your website visitors (or a competitor’s) with the subreddits they frequent. The communities that matter are rarely generic ones like r/marketing. They are niche ones: r/msp, r/accounting, r/sre, r/legaladvice, wherever your ICP goes to vent, ask for help, and compare tools.

Audit your SERP: Search your most important bottom-of-funnel keywords on Google and look for the Reddit threads ranking on page one. Those specific threads and those specific subreddits are your organic competitors right now. They are also your roadmap.

Use Reddit Trends and F5bot: Reddit’s Trends feature lets you monitor conversation volume and sentiment around your brand, competitors, and industry keywords. F5bot is a free tool that alerts you every time someone mentions your brand, a competitor, or a category problem you solve. Think of it as a backstage pass to real buyer intent, in real time.

Sort content in relevant subreddits by Top Posts: This immediately shows you what formats, topics, and tones have earned trust with your target audience. Ross Simmonds calls this the Sherlock Homeboy method: read what already works before you write a single word.

Phase 2: Build Karma Before You Say Anything Promotional (Months 2–3)

Reddit runs on a reputation system called Karma. Low karma accounts that show up with links or promotions get flagged instantly. You need to earn your standing.

This phase is straightforward. Find threads where people are asking questions you can genuinely answer. Answer them well. Not with a paragraph-long company pitch. With something that would help them even if you had no product to sell.

The goal is to build a presence that makes people want to click your profile.

On account setup: Ross Simmonds recommends maintaining three Reddit presences: your brand subreddit (r/YourBrandName, secure it now even if you do not use it), a brand account (u/YourBrandName for official responses), and a persona account with a real first name for authentic community engagement.

The persona account is the one doing the actual work.

Phase 3: Create Value, Not Traffic Bait (Month 3 onward)

The biggest mistake B2B marketers make on Reddit is trying to drive traffic off the platform. Reddit communities hate it and will downvote you into oblivion.

The winning format is what Ross Simmonds calls zero-click content: posts that contain the full value inside Reddit itself. No teaser. No read the full post here. Just the actual insight, complete and useful, with an optional link at the bottom for people who want to go deeper.

Posts that educate, engage, entertain, or give someone a new way to think about a problem consistently outperform posts that are really just thinly veiled lead gen.

When your contribution is genuinely useful, people click your profile. They find out who you are. They find your product. That is the conversion path on Reddit and it is opt-in.

The 9:1 Rule (Non-Negotiable)

HubSpot puts it plainly: for every one promotional or self-serving post you make, you need nine pieces of content that offer pure value. No agenda.

This is not just etiquette. It is survival. The volunteer moderators running subreddits will ban accounts that break this ratio, often without warning. And unlike a LinkedIn ban, a subreddit ban is visible to others.

What Gets You Banned (And How to Avoid It)

A few hard rules from the community:

Dropping links without context is an instant downvote trigger. Always explain why something is relevant and what the person will get from it.

Ignoring subreddit-specific rules. Every subreddit has its own rules in the sidebar. Some prohibit any self-promotion. Some require specific post flairs. Read them before you post anything. When in doubt, message the moderators.

Using polished, corporate-sounding language. Reddit is not LinkedIn. If your post sounds like it was written for a VC pitch deck, it will die in the comments. Plain language, peer-to-peer tone.

Cross-posting the same content across multiple subreddits without tailoring it. Each community has context. A post that lands in one subreddit can feel spam-like in another if it does not reference the community’s specific concerns.

Reddit is the new frontier of B2B, so plant your flag now

The brands that show up early get the unfair advantage. If you bring real value, play the long game, and speak like a human, the upside is absurdly outsized.

This is the kind of compounding presence that becomes a moat.
The kind that competitors later claim you got lucky with.
The kind that LLMs keep quoting long after you have forgotten you even wrote that comment.

Yours Promptly,
Manu

You could sprinkle “best something” a few times on a page and ride the Google wave.
Simpler times. But that world’s gone.

Answer engines like ChatGPT, Perplexity, and Gemini don’t rank. They reason.
They don’t ask who said it first; they look for who answered it best.

That shift isn’t subtle. It’s seismic.
The SEO/AEO (do you call it GEO?) playbook is changing fast, and as usual, we didn’t get the memo.

Webflow’s growth team (to their credit) is rewriting the rulebook quite openly.

This letter documents Webflow’s evolving AEO strategy, drawn from their team’s interviews, webinars, and case studies, including how they use AirOps.

From Keywords to Questions: The Shift to AEO

AEO has made the “tail” longer.

What used to be four to six-word queries now often stretch past twenty-five.

People no longer type. They ask.

As Guy Yalif (Chief Evangelist at Webflow) explained on GTM Now’s podcast, large language models like ChatGPT interpret full queries and even multi-turn conversations.

That means content can’t just sprinkle keywords anymore; it has to deliver complete, contextual answers that AI engines can confidently quote.

In practice, Webflow’s content team maps the real questions their audience asks at each stage and builds pages that answer them thoroughly: awareness, consideration, purchase, and beyond.

The result is content structured for humans but favored by machines.

Yalif also breaks the impact of this shift into three tough realities every marketer faces: loss of brand control as LLMs paraphrase content and often use wrong citations, lower traffic that’s already giving mini panic attacks to many, and a sea of sameness where it’s harder than ever to stand out.

Webflow’s AEO Framework

Webflow’s four-part AEO framework mirrors classic SEO fundamentals but tweaks them for the AI era.

Content: Create question-focused content that actually answers what buyers ask. Each page is semantically rich, easy to parse, and clustered by topic and intent. Webflow layers in on-page FAQs, structured markup, and original insights to stand out from generic, AI-scraped noise.

Technical: Make your structure readable for machines. Webflow adds schemas, FAQs (instant hit), how-to steps, definitions, and other rich snippets directly inline, helping AI engines extract clear, contextual answers without confusion.

Authority: Backlinks still matter, but brand reputation now carries more weight. Webflow sees every mention on Reddit, YouTube, tech blogs, or Q&A threads as an opportunity to shape how AIs understand and describe the brand

Measurement:Webflow monitors how often it is cited as a source in ChatGPT and other AI tools, tracks its share of voice versus competitors in AI-generated answers, and studies the sentiment and accuracy of those mentions.

The results speak for themselves. Self-signups from AI search channels grew by 8%. The traffic may be smaller in volume, but it is high-intent, with buyers arriving further down the funnel, ready to act.

Webflow ran quiet experiments and rewired its SEO playbook by keeping what worked, fixing what didn’t, and measuring everything in between.

The result was a set of simple but powerful moves.

1. FAQs and Schema

Two old-school SEO tactics found new life in the AEO era.

Vivian Hoang, who leads SEO at Webflow, shared how they turned something as basic as FAQs into a conversion and citation magnet.

On the surface, FAQs seem obvious: good for users, great for SEO, and now gold for LLMs. They help models understand and recommend your brand for mid to long-tail queries like “how does Webflow handle site migrations?”

The challenge, of course, was scale. Researching what people actually ask, writing clear answers, and formatting everything properly can take forever.

So Webflow’s team automated it with AirOps.

Here’s how their workflow runs: It starts by scraping the page to see what already exists. Then it pulls People Also Ask questions from Google, related queries from keyword tools, and real user questions from Perplexity, Reddit, and Quora.

The team even fed in questions from sales calls or user interviews, ensuring the FAQs reflect what real buyers want to know.

The result: structured, brand-aligned, schema-ready FAQs that live inline on pages. Webflow even runs a separate workflow to auto-generate schema markup, helping both search engines and language models understand the relationship between entities, products, and features.

Pages with new FAQ sections saw up to a 24% lift in traffic within two weeks, and nearly half of Webflow’s AI citations came from those pages.

Josh Grant, VP of Growth at Webflow, shared the AirOps workflow in his newsletter : Stacked: The AI Driven GTM Playbook.

AI crawlers love structured data. Schema helps them identify clear answers. FAQs anchor those answers to real buyer intent. Together, they make Webflow’s pages easy to parse, quote, and trust.

It’s proof that you don’t always need new content to win at AEO. You just need to help machines find better answers in what you already have.

2. Keep Content Fresh and Up to Date

Content freshness is now a ranking factor not just for Google but for AI engines too.

Pages updated more recently tend to be cited more often by LLMs.

“Last updated” is no longer a timestamp. It is a quality signal. (And no, changing the date on your blog doesn’t count.)

Webflow’s inbound engine had always relied on a strong library of evergreen content. But keeping it fresh was a grind: keyword research, rewrites, editorial approvals, CMS updates. Each refresh took days, so only a handful made it through every month.

Webflow (again) used AirOps to automate the refresh process without sacrificing quality or voice.

The workflow integrated directly with Webflow’s CMS, analyzing each article against search and AI trends to identify gaps in depth, structure, and clarity. It then suggested targeted edits that were brand-safe, context-aware, and ready to publish in minutes.

The result was clear. What was once a manual effort to refresh 40 to 50 articles a year scaled to dozens each month.

Freshness is not about rewriting everything. It is about learning faster, publishing smarter, and keeping your best answers visible where both people and machines can find them.

3. Brand Presence and Mentions

Backlinks still matter, but brand mentions now do the heavy lifting.

Webflow’s team realized that AI engines don’t just read your website; they read the internet’s opinion of you. LLMs pull context from YouTube, Reddit threads, G2 listings, and publications like TechRadar and Forbes.

Webflow’s comms team identified the most referenced sources in AI-generated answers and focused on showing up there. Business profiles on platforms like G2 and third-party sites were optimized for accurate features and positioning.

It also leaned heavily on Webflow University and creator-led content on YouTube, from affiliate tutorials to influencer walkthroughs, building a consistent presence across channels.

The takeaway is simple: brand authority is no longer built in isolation. It is distributed.

And in the AEO era, what the internet says about you is what the machine believes about you.

Measuring What Actually Matters

Webflow measures AEO performance across three dimensions: presence (are we in the conversation?), performance (are we driving traffic and signups?), and perception (how are AIs portraying us?).

It is a more nuanced scoreboard than traditional SEO rankings, but it ensures focus on meaningful growth instead of vanity metrics. After all, the endgame is business impact.

AI Citations and Presence: Webflow tracks when and where its content is mentioned or cited by AI systems. The team monitors ChatGPT’s visible sources, checks for references in Bing Chat, and uses tools that simulate AI queries to measure visibility. Each mention in an AI-generated answer is treated like a page-one ranking, a clear signal of authority and reach.

Share of Voice vs Competitors: Visibility alone is not enough. Webflow measures how often it appears as the source of truth compared to competitors in categories like website builders and CMS platforms. The higher their share of voice in AI-generated answers, the stronger their AEO moat becomes.

Traffic and Conversions from AI: Webflow tracks referral traffic from AI engines such as ChatGPT and Bing Chat, and measures how that traffic converts. The numbers tell the story. AI-driven signups increased from 2% in late 2024 to 8%, and those visitors converted about six times better than traditional organic traffic.

And here’s the kicker: Webflow achieved all this with the same SEO team, not by spinning a new wheel but by redeploying its expertise toward an AI-aware strategy.

Webflow is using the same SEO muscle but lifting weights differently for AEO.

It’s not about lifting heavier, it’s about mastering the technique.

Experimenting, refining, and getting better with every rep.

One rep at a time. One answer at a time.

Because good marketing is still about form, just a different kind of flex.

Yours Promptly,
Manu

A spreadsheet of dream accounts glows on the wall. Sales leans forward, half-nodding. Marketing scrolls through endless data and dashboards, debating who’s “in market” this quarter.

For the sake of progress, let’s assume everyone’s finally on the same page.

We know the idea works: focus on the right accounts, at the right time, with the right message. What’s changed is the precision.

Tools like Clay now connect the dots between data and timing, turning guesses into repeatable wins. It can bridge your CRM, enrichment layers, and AI systems, pulling signals together and surfacing patterns that used to drown in spreadsheets.

In this letter, we’ll look at how tools like Clay and a bit of AI precision can make ABM work in practice: how to create and enrich target lists, find the champions behind job titles, identify intent signals, personalize outreach and experiences through integrations, and finally, score the accounts that close.

Think of this as an ABM user manual broken into four stages that take you from identifying the right accounts to measuring what actually worked: Identify, Engage, Convert and Measure.

1. Identify (Hint: CRM is your best friend)

Ideally, every ABM play should start in your CRM.
It might look like a cluttered archive of names and numbers, but if you listen closely, it’s full of stories.

The ghosts of closed-lost deals whisper lessons most ignore.
Closed-won deals, meanwhile, help you backtest ICPs.

Series B SaaS companies with 500 employees and a growing RevOps team: that’s a pattern. Startups under 50 employees that keep churning: that’s another one, a warning sign.

Once you’re done playing Sherlock and identifying your target accounts, it’s time to turn those clues into a system.

1a) Building the Target List (finding who’s worth the chase)

Start with what you already know. Your CRM holds the blueprint.

Build a set of existing customer lookalikes (your true target accounts) inside Clay.

Filter by firmographics that actually matter: region, industry, employee size, funding stage, revenue band, and growth rate.

Strip out the noise with exclusion filters: off-region markets and small accounts that might look tempting but will never scale.

Output: a map of target account opportunities to pursue.

1b) Identifying Signals and layer with intent data (Account Intelligence)

This is where your list starts breathing. Use Clay to layer signals that help you separate companies standing still from those gearing up to buy.

Some of the account signals include:

• Hiring surges in GTM, RevOps, or product roles that hint at scaling pain.

• Funding events or new investment rounds that indicate fresh budgets.

• Job postings revealing priorities: Head of Demand Gen or RevOps Lead

• Tech stack data (CRM, HRMS etc.) using Clay’s technographic enrichments.

• Geographic expansion or new office locations that signal growth plans.

• Compliance milestones such as SOC 2 or ISO certifications that hint at enterprise readiness.

• Hiring freezes or layoffs, on the other hand, might indicate accounts to pause on.

Output: a scored sense of why now for each account, not just which accounts.

1c) Champion Hunting (People-level Intelligence)

Responsibilities and skills often reveal more than job titles do.
Use Clay to find the people who will actually move the deal.

Within Clay, filter people by job title, function, and seniority to identify who fits your buyer persona. Exclude interns, junior staff, non-buyers, or unrelated functions early to avoid cluttering your contact map. The goal is precision, not volume.

Then, layer intelligence across multiple dimensions:

• LinkedIn enrichments: search headlines, About sections, experience, and skills for high-intent keywords such as ‘ABM,’ ‘HubSpot,’ ‘RevOps,’ or ‘Pipeline.’ These details help you find the operators behind the strategy, not just the signatories.

• Certifications: spot credible professionals like ‘Salesforce Admin,’ ‘Inbound Certified,’ or ‘Marketo Expert.’ These credentials highlight hands-on expertise and influence in key buying decisions.

• Past companies: look for professionals who have worked with your competitors or used your product before. Prior exposure often means built-in trust and a shorter sales cycle.

• Experience and tenure: track how long they’ve been in their current role and industry. New hires bring change and new tools, while tenured leaders hold institutional control and decision weight.

• Education and languages: note relevant degrees, programs, or multilingual skills. These details help tailor outreach and show cultural awareness in international or cross-regional accounts.

  

  Output: Not just names but people annotated with real proof of expertise.

  

A lot of the heavy lifting can be done with Claygent, Clay’s own web scraper. You can prompt it to find exactly what you need: past companies, universities, cleaned URLs, or even specific keywords hidden in profiles and pages. It’s the easiest way to turn scattered web data into structured, usable context.

1d) Waterfall Enrichment (turn flat rows into full profiles)

Before Clay, enrichment felt like roulette. You copied a list into one tool, waited, copied the leftovers into another, prayed the emails would bounce less than last time, then repeated it all next quarter.

Clay ends that cycle with waterfall enrichment. You set the order, and Clay runs each provider in sequence, validating at every step. If Apollo fails, Clay tries Prospeo. If that misses, it moves to People Data Labs or LeadMagic.

Each layer checks deliverability and flags risky domains before you ever hit send.

The result is cleaner lists, live data, and lower bounce rates when paired with verification tools like ZeroBounce or NeverBounce.

2. Engage (from creepy personalization to relevance)

Everyone talks about being “personal.” Turns out, that’s the part that backfires.

Account-level personalization works.
Talk about their company, their market, their competitors.

Industry-level personalization scales.
Show you understand SaaS, fintech, or healthcare.

Pain-point-level personalization builds trust.
Focus on what keeps them up at night, not what they posted at brunch.

Personal-level personalization? Risky.
That’s not intent. That’s stalking. That’s just being a creep.

The goal isn’t to sound personal. It’s to sound relevant.

2a) Personalized Outreach (Smart, Not Spammy)

Personalization dies when outreach turns into a copy-paste contest. Clay keeps the system grounded by syncing every interaction back to your CRM. Paired with the right tools, it helps your outreach flow across channels without sounding repetitive.

Smartleads.ai: Pull enriched contacts straight from Clay, build custom sequences, and let AI handle timing, warmups, and follow-ups. You can even use Clay prompts to add account- and pain-point-level personalization like referencing a recent funding round or tech migration without typing.

Heyreach: Sync LinkedIn data from Clay, automate profile visits, connection requests, and follow-ups, and turn what looks like automation into intelligent sequencing. Heyreach lets you engage multiple stakeholders from the same account in parallel while keeping each thread personal.

Progressive Climbing: Treat outreach like a ladder

Start with mid-level managers, they respond faster, speak in problems, and often translate value into budget conversations for the decision-makers above them.

Managers are the footholds. Directors are the bridge.
Executives are the summit. Skip a step and you’ll slip.

Multi-Threading Without Chaos

Big accounts come with big committees. That’s where most ABM falls apart.

The CFO gets ROI math.
The CMO hears the growth story.
The VP of Ops sees integration clarity.
The managers get tactical enablement.

Each thread moves in sync. Pure orchestration.

In a Clay x MarketerHire bootcamp video, Bruno Estrella (Head of Marketing at Clay) and David Grieco (Director of Growth at Verkada) showed how you can build an ABM system that creates personalized landing pages and sends direct mail using Clay.

2b) Landing Pages (personalization at scale)

Marketing creates the building blocks: logos, tokens, content snippets, and templates. Sales fills a short form, selecting the use cases or industries they want to highlight.

Clay does the rest. It pulls the right pieces together and generates a live, personalized landing page instantly. (Works best if you’re using Webflow.)

No design queues. No “who’s updating the tagline” debates.

When something changes, like a case study, pricing update, or headline, you edit it once in the master template. Every live page refreshes automatically. Two hundred landing pages fixed before your next coffee.

The result isn’t just speed. It’s consistency adapted to the reader, a rhythm between personalization and precision.

2c) Direct Mail (that doesn’t lose Steam)

Verkada runs one of the sharpest direct mail programs in B2B, but even they hit the two enemies of ABM: friction and fatigue.

Address verification: when a company has twenty offices, finding where your contact actually sits in San Francisco and not at the HQ in Texas can take weeks of manual work.

Time: nothing kills sales momentum like telling a rep it will take six weeks to verify an address before a package can even be shipped.

The fix came from Clay’s waterfall model. It compares a contact’s LinkedIn location with the company’s office list and finds the closest match.

3. Convert (turning alignment into action)

Once you’ve opened doors with the right accounts, the next challenge is keeping the conversation alive, accelerating the deals already in motion, and most importantly, focusing on the ones that truly matter.

3a) Account Scoring and Prioritization: Use Clay to create live account scores and sync them back to your CRM. Clay stacks intent signals, enrichment data, and engagement metrics to tell you which accounts deserve dinner and which ones deserves an email. You can score them based on ICP fitment, signals like pricing page visits, email engagement, and more.

3b) Relationship Mapping (Clay × The Swarm): Find warm introduction paths to a target company or person. Leverage your employee, founder, and investor networks to surface who in your extended circle can open the door. Then route the outreach through those warm connections instead of cold requests that vanish into inboxes.

3c) Job Change (New Roles, New Priorities): Use Clay to track who in your target accounts has recently joined, moved, or been promoted. A new hire often means two things: they’re setting fresh priorities, and their previous company might already be your customer, giving you instant credibility.

3d) Competitive Positioning: Clay helps uncover what tools your target accounts are already using, especially for common categories like CRM, HRMS, or analytics. Use that data to craft messages such as “We know you’re on Competitor X. Here’s how we’re different.”

What gets measured gets budget

It isn’t about more dashboards or prettier reports.
It’s about knowing which moves actually mattered.

Once the plays are in motion, the question shifts from “what did we send?” to “what actually moved?”

In the Identify stage, measure reach and relevance. How many of your target accounts were activated, and how many truly fit your ICP? Track contact coverage inside each account. Too few means limited influence, too many without traction means misalignment. Use Clay signals to score accounts and help SDRs focus on the ones that show real intent.

In the Engage stage, measure momentum. Which accounts are opening emails, responding on LinkedIn, clicking through landing pages, or visiting your site? Every touch tells you how close an account is to buying readiness, the difference between curiosity and intent.

In the Convert stage, measure motion. Track how quickly engaged accounts move to pipeline, how much of that pipeline comes from your target list, and how those deals perform over time. Look at deal velocity, average deal size, and conversion rate by account tier. The story isn’t in the number of opportunities created but in how efficiently they turn into revenue.

When alignment meets evidence, budgets find their way home.

The Last Word

Some accounts will turn into deals (dream accounts come true).
Some will ghost you.
And some will ask you to ‘circle back next quarter.’

At the end of the day, the tools can surface patterns and stack signals
But the real skill is still awareness.

It’s knowing which accounts to chase, which ones to drop, and when to stop over-personalizing an email based on their cat’s name

Because great ABM is automation with intuition.

So stay hydrated. Stay caffeinated.

Yours Promptly,
Manu

The challenge isn’t to produce content. It’s getting it to produce content that feels on-brand, aligned to your ICP, and actually usable. And the more stakeholders you have (sales, product, leadership), the harder consistency becomes.

That’s the catch with AI “out of the box.”
It’s a Swiss Army knife. Versatile, yes, but not built for the precise cut you need.
It can write, summarize, and ideate, but not in your voice, not for your ICP, and not with your context.

Marketers don’t need generalists.
They need scalpels: GPTs trained for precision.

Which brings us to Custom GPTs.

Think of them as trained assistants you design once and then deploy across your team. Instead of re-prompting from scratch, you set the rules, feed in your docs, and make them shareable.

The payoff is clear:

• Consistency: Your messaging, tone, ICP, and positioning stop living in forgotten PDFs. They’re baked directly into the GPT so every email draft, battle card, or LinkedIn post starts aligned.

• Efficiency: You don’t have to handhold the model every time. Instead of wasting 20 minutes rewriting prompts, the GPT remembers your rules and gives you usable outputs faster.

• Shareability: This isn’t just for you. You can train a GPT once and then share it with product, sales, or demand-gen teams. Suddenly, the whole org benefits from a single source of truth.

Disclaimer: I’m using Custom GPTs as shorthand here, but the same logic applies to Claude Projects or Gemini Gems if that’s your stack.

The most important choice isn’t the model you pick.
It’s the context you feed it.

Every Custom GPT is only as good as the context you give it.
That context comes from two places: internal data (your tone guides, messaging docs, and product collateral) and external data (customer interviews, market insights, competitor intelligence).

Pair them, and it doesn’t just write like your brand, it mirrors your market.

Rachel Liske, a product marketing leader at HubSpot, explained this beautifully in a Marketing Against the Grain episode. Her team uses Custom GPTs and Claude Projects to support product marketing, and her framework breaks context into four simple layers, a system any marketer can adapt.

1. Customer context: Feedback from interviews, surveys, and support transcripts. This helps GPTs understand what prospects actually care about, their objections, and the exact language they use.

2. Product context: One-pagers, feature decks, and research docs. This ensures GPTs don’t just write in abstract but are grounded in what the product does and why it matters.

3. Internal context: Tone-of-voice guidelines, past campaigns, and social posts. This is how GPTs learn to write like you, not like a generic AI.

4. Market context: Competitive intelligence and positioning documents. This layer ensures GPTs don’t just parrot product features but frame them against what competitors are saying.

The real strength of HubSpot’s product marketing setup lies in how it pairs internal depth with external perspective. Here’s how that plays out in practice:

• ICP Builder GPT: Creates robust ICPs by combining internal data from sources like Google Drive documents and CRM records with external insights. Connect ChatGPT and HubSpot to get faster, data-backed ICP analysis.

• Messaging Coach GPT: Works like an always-on feedback loop trained in both brand tone and customer truth. It reviews messaging to uncover blind spots marketers often miss and helps answer the question that matters most: Would our ICP actually care about this?

• Positioning GPT: The HubSpot team creates Claude Projects for each product and customer persona, inputting extensive data to update positioning documents based on the latest research. This data is refreshed quarterly to keep up with evolving customer expectations.

Dave Gerhardt’s Exit Five newsletter featured the Event Validation Assistant by Caroline Ang, Senior Director of Marketing at AidKit.

• Event Validation GPT: It evaluates which events to attend, sponsor, or skip. The GPT weighs budget, audience relevance, and competitive presence to produce a crisp executive summary with fit scores, key deadlines, and a deeper analysis of cost, audience, and strategy. In minutes, it gives recommendations aligned with her ICP and goals, replacing what once took hours of manual research.

Then there’s Cognism, whose marketing team has built a set of lightweight GPTs that automate the repetitive but essential parts of their workflow.

• Case Study Finder GPT: Acts as an on-demand sales enablement assistant trained on Cognism’s case study library. Anyone on the team can ask, “Show me UK-based SaaS case studies with pipeline impact,” and get a ready shortlist with links. No more digging through folders or pinging the marketing team, just quick answers.

• Tone of Voice Reviewer GPT: Paste in any draft, whether a blog, ad, or email, and it reviews structure, tone, and clarity against Cognism’s brand guidelines. It flags what’s off, highlights what’s working, and suggests cleaner phrasing. Since rollout, review cycles have dropped, and brand voice now stays consistent across every channel.

• Landing Page Generator GPT: Built around Cognism’s HubSpot layout and messaging frameworks, it drafts complete landing pages in minutes. Feed it a campaign theme and target keywords, and it returns conversion-ready copy with CTAs, proof points, and links to relevant case studies. What used to take hours now takes minutes, without losing quality or tone.

How to actually build one

Creating a Custom GPT isn’t a technical maze.
It’s more like onboarding a new team member who never forgets a brief.

Step 1: Create and set context
Head to ChatGPT and click Explore GPTs → Create.
Think of this as opening a new campaign brief.

Decide what kind of assistant you’re building: a Launch Planner, Messaging Coach, or Competitor Intel GPT.

Describe its purpose clearly: “Act like a senior product marketer who analyzes competitor messaging and surfaces positioning opportunities.”

Then define the essentials:
How it behaves: clear, confident, and analytical.
What to avoid: jargon, filler, or generic summaries.

Step 2: Configure and feed context
Now give it depth. Upload your brand assets, messaging house, ICP docs, tone guides, and win/loss notes. The more context you feed, the more it starts to think like your team. For example, upload your positioning framework and ask it to refine a competitor comparison for your next GTM deck.

Optional: Add capabilities
Turn on Web Search to pull live competitor insights or Image Generation to mock up campaign visuals.

Suddenly, your GPT isn’t just writing. It’s collaborating.

So, where to start?

Start by asking, “What do I do every week that eats hours but follows a predictable pattern?”

At its core, this isn’t about prompts or models.

It’s about leverage.

To spend less time formatting copy and more time shaping the story.
To stop fixing decks and start defining narratives.
To shift from the what to the why of marketing.

Because the marketers who’ll win the next chapter aren’t the ones who prompt best, but the ones who build the best systems around their prompts.

And once you’ve got one saving you hours a week, you’ll suddenly have more time to doomscroll Reddit threads... I mean, ship campaigns.

Yours Promptly,
Manu

When it comes to the blank canvas problem, ChatGPT is handily the best antidote marketers have ever had.

With a single prompt, that empty page explodes into ideas, outlines, even full drafts.

But here’s the catch: not all outputs are created equal.

Why do some people get outputs that read like McKinsey slides while others fall completely flat?

The answer is context.

Think of it like photography. Two people can stand in the same location, holding the exact same camera. One takes a shot that ends up framed in a gallery.

The other takes a blurry photo of their thumb.

The difference isn’t the gear. It’s the aperture, the shutter speed, the framing, the eye for detail.

Prompts work the same way. The tool stays constant.

But the way you set the stage, the context you feed in, changes everything.

Think of them like Lego blocks you can assemble differently depending on the task.

Here are the big ones:

Role: Tell the model who it is supposed to be. A consultant, a copywriter, a developer, a B2B CMO, a stand-up comedian. Roles change the entire flavor of the response.

Tone: Specify how it should sound. Formal, playful, sarcastic, data-driven. Without this, you get LinkedIn Beige by default - technically fine, instantly forgettable.

Format: Is the output a 200-word essay? A table? A 3-step plan? A tweet? Context isn’t just about what you say, but how you want it delivered.

Audience: Who is this for? A CEO? A customer? Different audiences require different levels of detail and persuasion.

Constraints: Word count limits, avoid jargon and clichés, include sources. Constraints sharpen the output.

Examples: Few things help more than saying, “Write it like this example.” Models love patterns. Feed them one and they’ll follow.

Common prompt frameworks, aka the Context field guide:

C.R.I.S.P.
Clarity: Be clear about the task.
Role: Assign the AI a persona.
Instructions: Spell out steps or constraints.
Scope: Define the boundaries (length, detail).
Purpose: State the goal or why you need it.

P.R.O.M.P.T.
Persona: Who should the AI act as?
Role or task definition.
Output format (bullet list, table, narrative).
Modifiers: Tone, style, voice.
Parameters: Constraints, limits, do’s and don’ts.
Target: Audience or intended reader.

R.E.A.C.T. (for reasoning tasks)
Reasoning: Ask the model to think step by step.
Examples: Give samples to learn from.
Action: Specify what you want done.
Constraints: Set boundaries.
Test or Verify: Ask it to self-check or validate.

Chain of thought (CoT)
Ask the model to think out loud, show reasoning, or break down steps before the final answer. Improves logical and complex outputs.

A.C.T.
Assume a role.
Create with constraints.
Tailor to audience.

P.A.S. / A.I.D.A. adaptations
Borrow from copywriting frameworks like Problem–Agitate–Solution or Attention–Interest–Desire–Action, baked into prompts.

Roleplay / simulation
“Pretend you are X.”
“Act as if you are conducting a workshop with Y audience.”

Deliberate iteration
Step 1: Generate a baseline.
Step 2: Critique the output.
Step 3: Refine the prompt with new constraints.
Step 4: Repeat until good enough.

Meta-prompting
Ask the AI to propose better prompts or suggest ways to refine your input.

But here’s the thing: you don’t need to use every trick in the book.

Start small. Pick one framework that feels natural.

And the next time you type into that blank chat box, ask yourself:

Am I giving this the detail it deserves?
Or am I about to get back a blurry picture of my thumb?

Yours Promptly,
Manu