Editing Openai/6969614e-00e4-8001-ac42-dda7410c3c4a (section)

=== Assistant: Yes — that helps a lot, and it puts you well past the point where the usual criticism of meta-prompts applies. ===
Yes — that helps a lot, and it puts you well past the point where the usual criticism of meta-prompts applies.

What you’re doing is no longer a vague meta-prompt. It’s a role-scoped epistemic constraint. That’s exactly the right move.

Here’s how to think about it precisely.

==== When you say: ====

: 

you are constraining:
* Knowledge domain (doctrine, cases, statutes)
* Reasoning style (rule → application → policy)
* Standards of proof (authority, precedent, ambiguity tolerance)
* Default audience assumptions (educated, skeptical, literate)

That sharply reduces the model’s degrees of freedom.

You’re telling it which mental toolkit to use, not just “be smart.”

==== Even within a specified role, there are still internal forks: ====

===== - doctrinal vs critical =====
* teaching vs scholarship
* bar-prep vs theory
* survey vs deep analysis
* neutral exposition vs adversarial critique

So this:

: 

is good.

This:

: 

is excellent.

You don’t need to overdo it — one extra clause usually stabilizes behavior.

==== You’re implicitly doing three things right: ====
# Role scoping – you limit the knowledge base
# Norm signaling – you imply rigor and precision
# Audience calibration – you suppress beginner explanations

That’s why you see:
* shorter responses,
* tighter framing,
* fewer tangents,
* more appropriate depth.

For many tasks, that’s enough.

==== Drift still appears when: ====
* the task switches from analysis to generation,
* the topic crosses disciplines (e.g., law + economics),
* the response length matters a lot,
* you want consistent formatting across many prompts.

That’s where templates or charters outperform even well-specified roles.

But for one-off or semi-repeat work?
Your method is solid.

==== If you want maximum return for minimal effort, add one of these, not all: ====
* a length constraint
* a mode (“critique,” “explain,” “evaluate”)
* an audience clause (“assume X already knows Y”)

Example:

: 

That’s already doing 80–90% of the work.

==== Yes — specifying the type of professor or knowledge worker meaningfully improves precision and consistency. ====

At that point:
* meta-prompts are no longer vague,
* they’re functioning as legitimate cognitive constraints.

You only need heavier structure when:
* you want repeatability across many sessions, or
* small deviations matter.

In short:
You’re using meta-prompts the right way.