How I Trained an AI to Help Me Think Through Product Content (Not Write It)

Writing product-led content sounds simple in theory—just explain how your product solves a problem, right? But anyone who’s actually done it knows the struggle.

The challenge isn’t just the writing. It’s the hunting. Hunting for the latest sales deck. For the right positioning statement buried in a Notion page from three quarters ago. For the Slack message where someone from the product team casually dropped a killer feature insight—two weeks back.

The bottlenecks are everywhere. You’re waiting on replies from sales or product folks who are swamped. You’re dealing with outdated docs, inconsistent messaging, or worse—zero documentation for something new that just went live.

As a content marketer, especially one focused on speed and accuracy, this gap between information and execution can be exhausting. Product-led content only works when it’s precise and rooted in the product’s real value. But how do you write confidently when you're constantly second-guessing the source?

That’s when I knew I needed something better—and more scalable—than just sticky notes and scattered files.

Why I Built a Custom GPT Agent

After one too many back-and-forths trying to confirm a feature detail, I hit a wall.
Writing started to feel like detective work—piecing together clues from messy folders, outdated decks, and Slack threads with no context.

The process was slow, fragmented, and mentally draining.

That’s when the idea clicked.

So I did just that.

I created a custom GPT agent and fed it everything—from sales decks and feature one-pagers to internal marketing docs and battlecards. My goal wasn’t to automate writing. It was to eliminate guesswork. I wanted to create product content that was smarter, faster, and rooted in truth—without needing a green signal from five different people.

But I also saw another opportunity: what if this agent could help not just me, but every writer on the team? Instead of interrupting product managers or wading through endless folders, they could simply ask the bot—and get instant clarity. A knowledge base that talked back.

That’s when this stopped being just a writing tool. It became a scalable system for smarter content creation.

The Build: How I Created It

I used Custom GPT on ChatGPT to bring this idea to life. The setup was surprisingly simple—what mattered more was what I fed it.

I started by gathering the most frequently referenced (and most often lost) materials:

• Sales decks
• Product one-pagers
• Use-case documentation
• Battlecards and objection-handling sheets
• A few internal FAQs and positioning notes

Everything that a writer would typically need to ask someone about, I uploaded.

To make things easier for the model—and for my own sanity—I loosely structured the data into buckets:

• Features & Benefits
• Competitor Comparisons
• Customer Use Cases
• Positioning & Messaging
• Objections & Sales Narratives

One surprising challenge was realizing how inconsistent some of our internal docs were. Some decks were outdated. Some positioning language clashed. It forced me to do a mini content cleanup before uploading anything—because garbage in, garbage out.

But once it was live? Game changer. I could ask, “How does our product help with influencer background checks?” or “What’s our edge over [competitor]?”—and the bot would pull together an informed, aligned response in seconds.

How I Use It Today

Now, this custom GPT agent sits at the heart of my writing workflow. It’s not replacing me—it’s supercharging me.

Here’s what I use it for almost daily:

• Writing use-case-driven blogs
  I can ask things like: “How does our platform help with social media background checks?” or “Give me a breakdown of our use cases for influencer vetting.”
  It pulls from everything we’ve uploaded and gives me a clear starting point, tailored to our product.
• Drafting feature highlights
  When we launch something new, I can just ask: “Explain [Feature X] in 2 lines for a product update blog,” or “Summarize [Feature Y] in a benefit-driven headline.”
• Comparing with competitors
  Instead of scrolling through old decks or Notion pages, I ask: “How are we different from [Competitor Name]?” or “What’s our key advantage over XYZ when it comes to creator data?”
• Crafting CTAs and value-driven statements
  I even use it to test messaging: “Write 3 CTAs emphasizing speed and accuracy,” or “How would you position our product to a brand vs. a platform?”

The time I save is massive. What used to take hours of digging, confirming, and second-guessing now takes minutes. But more than just speed, it gives me clarity—consistency in tone, positioning, and product facts across everything I write.

Some of my go-to prompts:

• “What are 5 questions a skeptical customer might ask about our product?”
• “What assumptions do we make about our users that might need rethinking?”
• “Who are our top 3 user personas, and what does success look like for each of them using our product?”
• “What words, phrases, or angles should I avoid when positioning our product to an enterprise audience?”
• “List common objections from sales calls and suggest how our product addresses each one.”

What I Learned

Creating a custom GPT wasn’t just about writing faster—it completely changed the way I approach product content.

One of the first things I realized: AI is only as good as the inputs you give it. I had to clean up outdated decks, align inconsistent messaging, and rethink how we were documenting product details internally. That cleanup alone improved not just the AI agent’s output—but also how our entire team talks about the product.

I also learned that AI doesn’t replace thinking—it supports it. While the agent could surface answers, angles, and comparisons, I still had to shape the narrative, connect with the reader, and add the human context that content demands. It became a collaborator, not a shortcut.

Another unexpected insight? It became a knowledge hub for the whole team. New writers could onboard faster. Sales team members started using it to clarify positioning. Even the product team found it helpful for double-checking phrasing before launch notes went live. That alone made the build worth it.

But the biggest win? Clarity. No more second-guessing whether a blog accurately reflects our positioning. No more messaging chaos across channels. The custom GPT helped us speak with one voice—and speak faster.

What’s Next?

Building the custom GPT agent was just the start. Now that it’s part of my daily workflow, I’m thinking about how to make it even more powerful—not just for me, but for the entire team.

I’m also exploring adding prompts for strategy-level thinking—like guiding content ideation based on gaps in our blog, or helping shape product messaging for new personas we’re targeting.

Another idea? Turning the bot into a resource for cross-functional teams. Imagine a marketer, salesperson, or even a customer success rep being able to ask:

“How do we position [Feature X] to a fintech company concerned about compliance?”

That’s where I see this heading—a dynamic knowledge assistant that helps every team member speak the same product language.

And maybe down the line, I’ll train a second GPT agent—this time just for SEO content strategy. But for now, this one is already changing the game.

That’s it for this edition of The Open Diary of an AI Marketer — where I share the real, messy, experimental side of using AI in marketing.

Thanks for reading, and I’ll see you in the next entry.
Until then, keep building, keep tinkering. 🚀

— Charu