Ask The Game, the Build Log

From Minutes to Meaning: How I'm Restructuring The Game Podcast

How I Segmented 3 Podcast Episodes by Topic (And Why I'm About to Do 915 More)

Here's the thing about podcasts. Most platforms organize them by time. Spotify shows you timestamps. Apple Podcasts too. That makes perfect sense for them.

But what if you want to search through 915 episodes? Time-based organization breaks down fast.

I have this same problem. My system takes each episode and splits it into small pieces — 30 to 90 seconds each. I have to do this. Search systems and databases work better with bite-sized chunks than giant 60-minute transcripts.

The problem? I was only using time to split things up.

Alex might spend 4 minutes explaining one pricing concept. But my system would chop that into three random pieces just because of the clock.

Or worse: one chunk starts with pricing advice, then Alex changes topics halfway through.

The result? Search was terrible. Quotes got cut off mid-thought. Summaries made no sense.

How I Taught AI to Understand Podcast Structure

I built something new. Topic segmentation. It's the first system I know that understands what's actually being talked about in podcast content, not just when it was said.

How it works:

My pipeline is live now. And it works.

I Tested It on Real Episodes

I didn't just test on one episode. I processed 3 full episodes with different styles:

The system handled each one perfectly.

It caught the "BIG DECISION" announcement moment. It split a debate about manifestation vs action cleanly. It spotted when Q&A turned into personal storytelling.

The results were spot on:

No crashes. No weird segments. Just clean, useful structure.

What This Makes Possible

Now that episodes can be split by actual topics instead of random timestamps, I can build cool features:

It's like putting chapters in a book that never had them.

Why Structure + AI = Game Changer

Here's the thing: topic segmentation is just step one. The real magic happens when you add AI reasoning on top.

The problem with audio: Working with podcast transcripts is way harder than text documents. Here's why:

Most AI can handle "What does this book say about pricing?" because books are organized. But ask "What does Alex say about pricing across 915 episodes?" and even the best AI struggles. It's drowning in messy audio chaos.

My theory: Structure all 915 episodes first. Then add AI reasoning on top. Instead of making AI work with messy transcripts, organize the chaos into clean topics first.

What this could enable:

I built a simple chatbot on messy transcripts before. It kinda worked. But with structured topics as the foundation? That's when this becomes something totally new.

Why audio is so hard: You need perfect transcription, speaker ID, timing, topic boundaries, confidence scores, and context preservation. I just solved all of that. Now I can add reasoning on top of solid structure instead of messy, unorganized transcripts.

What's Next: Adding the AI Brain

Once I have all 915 episodes structured, the real experiment starts: building the AI reasoning layer.

I need to learn a lot here. I'm looking at tools like LangChain to connect AI with my structured podcast data. But honestly, I'm still figuring out the best way.

The experiments I want to run:

The technical stuff I need to figure out:

This is new territory for me. But that's what makes it fun.

Another Real Problem I'm Solving

Here's what the UX designer in me is also excited about. This fixes something that drives me crazy about current AI tools.

The problem: AI gives you summaries and insights, but can't show you where it found that info. I don't want a vague summary. I want the exact paragraph where you got that insight.

My solution: With topic boundaries, I can build AI that doesn't just understand The Game podcast content. It can send you directly to the 2-minute segment where Alex explains his exact framework. Then you can listen to it.

Imagine asking: "What does Alex say about pricing psychology?"

Instead of a generic summary, you get:

This is what I'm building. A tool that understands the podcast better than anyone, including Alex. But it can prove it by showing you exactly where every insight comes from.

Why I'm Really Doing This

If you're a podcaster, this opens up cool possibilities:

That's cool, but not what I'm after.

What I really want to fix is my own problem as a listener. I want to have deep, meaningful conversations with podcast content and get sent to the exact moment where that insight was discussed.

This topic segmentation breakthrough gets me one step closer to building the podcast intelligence system I've always wanted.

915 episodes, here we go.

— Benoit Meunier