Meta-prompting
Don't write a good prompt — hand over all the raw material and let the AI carve it down. Nowhere in the three steps is the result measured.
The method is right, and nowhere in its three steps is there a place that measures whether the prompt it produced is any good. A sixteen-minute video lays out one person’s meta-prompting style — don’t try to write a good prompt; hand the AI every scrap of raw material and let it do the carving. Half concept, half live demo. What I actually took from it was not the procedure but one sentence about what the question-eliciting step is really for, and what I couldn’t take was any evidence that the method pays off.
What the video says
The definition is one line — have the AI produce the prompt you’ll feed to the AI. Not “build me X” but “I’m going to build X; write me an optimised prompt worth feeding to an AI.”
And three things to get right.
- Elicit questions. Don’t stop at “write me a prompt”; append “if you need more context to write a good prompt, ask me.”
- State success criteria. Not “make me an awesome landing page” but “the core value has to be visible in the first mobile viewport, and every button and form has to actually work.”
- Convert for the execution environment. Each destination tool needs something different — stop conditions for one, constraints for another, framing and lighting and camera for image generation, source standards and verification method for research.
Naming the antipattern was the good part. Going straight in with “I’m building a CRM, write me a meta-prompt” leaves you trapped inside a frame the AI invented on its own. Nothing of yours went in, so nothing of yours comes out, and once a frame sets you only ever edit within it.
One practical tip landed. If you have requests from a meeting, paste them verbatim rather than tidying them up. The moment you refine, only as much as you understood survives.
The technique — why expand first, then cut
Writing 4,000 characters from the start preserves far less detail than expanding fully and then carving down to 4,000. The presenter’s analogy is exact — not being able to write long is the beginner’s problem; the hard part is cutting a long piece down to its points. It’s why school sets minimum word counts and university sets maximums.
And then you start in a fresh window. “Every LLM is at its best early, when the least context is loaded.” There’s no reason to drag along the debris accumulated while building the prompt.
The reason the front end matters more on long jobs is stated too. If you’re going to burn tokens on something that runs for days, starting in the wrong direction costs proportionally more.
What broke when I checked
The compression criterion can’t be verified after the fact. “Can the AI decide this dynamically without it being in the prompt?” is a clear rule to apply, but when you cut something and the result comes out fine, nothing distinguishes “safe to cut” from “got lucky.” You’d have to run the same request with and without, and the video contains no such comparison.
And the payoff has never been measured. Nowhere in sixteen minutes is there a figure for whether this beats not doing it, or by how much. The presenter closes by saying “even having done all this, I’m not sure it’s worth teaching” — and that honesty is what raises my confidence in the material. It’s a style share, not a product.
The most valuable sentence is about a side effect, not the procedure. Having asked for questions, no prompt came back — a flood of questions did, and the presenter calls that “already a huge success.” Then only three get answered and the rest are delegated. The line that follows is the core of the video.
Looking at just questions 1, 2 and 3, you can see how thin my original information was.
The real body of the question-eliciting step isn’t a better prompt — it’s showing me where the holes in my own context are. That is a diagnostic instrument, not a prompting trick.
⚠ Though delegating everything makes it pointless. The presenter warns about this: hand it all over and the reason for running the procedure disappears.
Held against my own setup
Steps ① and ② I already had, in code. My pipeline drops the source spec in whole and unrefined, then a deterministic stage cuts it to what’s needed. The shape is the same — except the thing doing the carving is code rather than an LLM, so what got cut and why is reproducible.
Step ③ I hold more strongly. Reviewing the artifact doesn’t end at a human edit; it’s a gate. That is precisely the slot missing from these three steps.
The one thing I didn’t have is single. I have always used “ask me questions” to get better output, never to see what I left out. Same sentence, different purpose, and you read the answer differently.
Verdict
| What | Call |
|---|---|
| Question-eliciting as a context-hole diagnostic | Adopt. There’s a slot for it before a spec goes in |
| Expand first, then cut | Already doing it. Difference is that code does my carving, so it reproduces |
| Start in a fresh window | Already doing it. The stated reason is worth keeping |
| Adopt the three-step procedure wholesale | Unnecessary. ①② exist and ③ is stronger on my side |
The lesson this piece paid for: methodology videos are worth something for their side effects, not their procedures. The three steps were things I was already doing. What survived was one line thrown out in passing — “three questions in, you can see how thin my information was.”
And a method with no place to measure gets neither better nor worse. Whether this pays off is currently unknowable, and finding out means running the same request both ways. The presenter ending on “I’m not sure” is, for that reason, an accurate self-assessment.