I built 90+ AI prompts because raw transcripts are useless

CopperSunDev — Thu, 18 Dec 2025 00:29:25 +0000

A few weeks ago I posted about building a transcription tool. The responses were helpful. A few people asked about what I do with the transcripts after.

Honest answer: for a while, not much.
I'd get this wall of text with speaker labels and timestamps, and then... stare at it. The transcription part worked. But a raw transcript is like having all the ingredients dumped on your counter. You still have to cook.

The problem I kept running into

I do interviews for work. Marketing stuff mostly. The goal is usually to turn a 45-minute conversation into something publishable—a blog post, social clips, whatever.

So I'd paste the transcript into Claude or ChatGPT and say something like "turn this into a blog post."

The output was... fine? Generic. It would summarize instead of pulling actual quotes. It'd lose the person's voice. I'd spend an hour fixing it and think "I could've just written this myself."

Same thing with meeting notes. "Summarize this meeting" gets you a summary. But what I actually needed was: what did we decide, who's doing what, and what's the follow-up. Different problem.

So I started building prompts

Not because I planned to. I just kept tweaking the same prompts over and over until they actually worked.

The blog post one took the longest. I needed it to:

Keep the interviewee's actual voice (not sanitize everything into corporate speak)
Pull real quotes, not paraphrase everything Structure it like a real article, not a book report
Lead with something interesting, not "In this interview, we discussed..."

That one prompt went through probably 15 versions before it stopped annoying me.
Then I built one for meeting summaries that extracts decisions and action items separately. One for turning podcasts into social posts. One for cleaning up the speaker labels in raw transcripts.

At some point I looked up and had 90+ of them.

What I learned about prompting

Most of my early prompts were too vague. "Summarize this" doesn't tell the model what you actually care about.

The ones that work best are almost annoyingly specific:

What's the exact output format?
What should it include vs. ignore?
What tone? What length?
What questions should it ask me before it starts?

That last one was a breakthrough. The best prompts don't just run—they clarify first. "Before I process this, tell me: how many speakers, what are their names, what's the context?"

Turns out you get way better output when the model understands what it's working with.

Some that ended up being useful

A few I keep coming back to:

Transcript cleaner — Takes raw output with "Speaker 0" and "Speaker 1" labels and turns it into something readable with real names and proper formatting. Sounds trivial but it's the one I use most.
Interview → blog post — Extracts the interesting parts of a conversation and structures them into an actual article. Keeps quotes intact. Writes transitions that don't sound like AI wrote them (usually).
Meeting action items — Pulls out decisions, tasks, and owners from a meeting transcript. Ignores the 40 minutes of small talk to find the 5 things that actually matter.
Podcast social package — Generates a batch of social posts from an episode transcript. Quote cards, discussion questions, that kind of thing.

I also built some weird specific ones for legal transcripts (deposition analysis, contradiction detection) that I'm not sure anyone else needs. But they exist.

Where they live now

I put them on GitHub and linked them from the transcription site:
https://brasstranscripts.com/ai-prompt-guide

They're organized by use case. Some have full write-ups explaining how to use them, others are just the prompt.

They work with Claude, ChatGPT, Gemini—whatever. The transcript format matters more than which model you use.

Still iterating

Some of these are solid. Others I'm still not happy with. The social media ones especially—getting an LLM to write something that doesn't sound like an LLM wrote it is its own challenge.

If you've built prompts for processing transcripts (or any structured text really), curious what approaches have worked for you. The "ask clarifying questions first" pattern has been the biggest improvement for me, but I'm sure there are techniques I haven't tried.

I ended up building a transcription tool

CopperSunDev — Fri, 12 Dec 2025 20:03:59 +0000

I do a lot of interviews with subject-matter experts for work. Usually it’s over Teams or Zoom. Sometimes the built-in transcript is missing, locked, or just unusable.

For a while I tried the usual options. Some required subscriptions I didn’t want. Others had weird formatting that meant I spent as much time cleaning up the output as I would have just typing it myself. A few couldn’t handle multiple speakers without turning it into a mess.

At some point I was messing around with Claude Code and thought: why not just build something myself?

That turned into a lot of hours, a bunch of blind alleys, and more tweaking than I expected.

*What actually worked
*
Speech recognition has gotten surprisingly good in the last few years. The open source options are solid now. Getting accurate text from clear audio isn’t the hard part anymore.

Speaker diarization was trickier. Figuring out who said what in a conversation is a different problem than just converting speech to text. Getting those two pieces to work together cleanly took more debugging than I’d like to admit.

*The stuff I underestimated
*
Audio quality variation.
A clean studio recording and a laptop mic in a conference room are completely different problems. I spent a lot of time on preprocessing that I didn’t plan for.

**Output formats.
**I originally just wanted plain text. Then I needed SRT for a video project. Then JSON for piping into other tools. Scope creep is real, even on your own projects.

**Edge cases with speaker detection.
**Two people with similar voices. Someone who talks over someone else. Long pauses where the model isn’t sure if it’s a new speaker or the same person thinking. These are harder than they sound.

*Where it’s at now
*
Eventually I had something that worked well enough for my own use, so I turned it into a small platform:

https://brasstranscripts.com/

It takes audio or video and produces transcripts that are actually usable. Mostly I cared about speaker separation and output that didn’t need a lot of cleanup afterward.

No subscription — just pay per file. I built it that way because that’s what I wanted as a user. I transcribe maybe five to ten recordings a month. Paying $20/month for that felt wrong.

I’m sure there are edge cases I haven’t hit yet. I’m still adjusting things as I run into them. The diarization in particular is something I keep tweaking.

Posting here mostly in case it’s useful to anyone else who runs into the same problem. Or if you’ve built something similar and have thoughts on approaches I should try.

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