Paste four sentences into your extraction prompt and see if it can tell an exercise apart from an ad read. That’s the whole test, and most prompts fail it in under ten seconds. A Redditor going by u/Dear-Skirt9403 built a tiny podcast snippet to check exactly that, and it’s a sharper diagnostic than it looks at first glance.
The setup: a made-up transcript with one real exercise (“write down your prediction”), one sponsor pitch, and one exercise the host tells listeners to skip. If your prompt hands back all three as “action items,” it’s not extracting exercises. It’s just listing sentences that contain verbs.
🎧 Run The Check Yourself
Here’s the transcript the original poster used to build the test:
Pause and write down your prediction before we play the result. Our sponsor has an offer for listeners. Visit their site and enter the code. Back to the lesson. The book suggests drawing a diagram here, but skip that exercise for today’s session.
Three candidates hide in that paragraph. Only one of them should survive a well-built prompt.
📝 Step By Step
- Copy the transcript above into a text file or a fresh chat.
- Paste in the author’s prompt exactly as written.
Read the transcript below and list the practice exercises intended for listeners. Quote each instruction. Leave out sponsor requests and exercises the speaker says to skip. Include an exercise quoted from another source only if the speaker asks listeners to do it. Put unclear cases in a separate section for review.
- Paste the transcript underneath the prompt, then run it.
- Check the output against one rule: it should return exactly one exercise, the prediction line, quoted word for word from the source.
If the sponsor code shows up as a task, or the “draw a diagram” step sneaks into your list, the prompt collapsed. That’s your fail state. Plain and simple.
🔍 What Your Results Actually Mean
A clean pass here proves less than it feels like. One reply from u/lib3rat0r called this out directly: the snippet basically narrates its own boundaries out loud. “Our sponsor has an offer,” “back to the lesson,” “skip that exercise” are near-flashing signs. A prompt that just pattern matches on the word “sponsor” can pass this exact test without understanding intent at all.
So treat a clean result as a floor, not a ceiling. It tells you the prompt can follow explicit instructions when the transcript hands them over on a plate. It says nothing about whether the same prompt can catch a host who slides into a product recommendation mid-explanation, with zero announcement. That harder version is what actually separates a good extraction prompt from a lucky one.
Think of this test as a smoke check, not a certification. Pass it, then go looking for the transcript that’s designed to make you fail.
Worth noting: this is exactly why “list the action items” was too loose for this job in the first place. Sponsors give listeners actions too, codes to enter, sites to visit. Any prompt that treats “action” as the filter will scoop those up right alongside the real exercise. Swapping “action items” for “practice exercises intended for listeners” is a small wording change that does most of the heavy lifting here.
💡 Extra Tips
- Build your own harder snippet. Have the host recommend a product mid-story, no “our sponsor” flag anywhere nearby, and see if the prompt still catches it.
- For real episodes, skip auto-generated captions whenever you can. The original poster suggests pulling the publisher’s transcript, or transcribing the audio with Vomo AI. Raw auto-captions blur the sentence boundaries this whole prompt depends on.
- Manually skim the minute or two right after any ad read before you trust the model. That’s exactly where hosts drift back into content, and where your prompt is most likely to grab a stray sponsor line by mistake.
- Ask the model to quote instructions verbatim instead of summarizing them. Summaries tend to blend the “skip this one” caveat right into the exercise description. Do that, and you lose the exact distinction you built the prompt to catch.
- Keep an “unclear” bucket in every extraction run, the way the original prompt does. Forcing borderline cases into a review pile beats silently guessing wrong.
🏴☠️ Your Move
Run the test above right now, it takes less time than your coffee is getting cold. Then build a meaner version: a host who buries a sponsor plug mid-sentence, no signposting at all. If your prompt survives that one, you’ve got something worth trusting on a real episode. Drop your toughest transcript in and see what breaks!
Frequently Asked Questions
Q: How do I know if my prompt is actually reasoning or just keyword-matching?
Test it with something harder. If your test snippet literally says “Our sponsor,” you’re not really testing reasoning, you’re testing pattern matching. Write an adversarial version where the same concept sneaks in naturally (“go try their free tier tonight, it’s the same principle we covered”). If your prompt still excludes it, that’s reasoning. If it includes it, keyword matching was the whole story.
Q: I wrote a prompt with four rules in it, and something went wrong. How do I figure out which part failed?
Design one test case per rule. When everything’s bundled together and the output is wrong, you’re debugging blind. Split it up: one test for “exclude sponsors,” another for “exclude skipped exercises,” etc. Takes a little longer upfront, but you’ll know exactly which rule broke.
Q: I added an “unclear cases” section to my prompt, but the model never uses it. Why?
It probably needs a trigger condition. Without one, models quietly force ambiguous stuff into your main buckets instead of flagging it. Be specific: “put cases in this section when you can’t tell if it’s an exercise or when rules conflict.” Give it a reason to exist, and it will.
A small test for extracting exercises from a podcast transcript
by u/Dear-Skirt9403 in PromptEngineering