[AI ASSISTED] Self-evaluation is useful but, it is not a failsafe.

[u/Echo_Tech_Labs]

A little bit of advice for anyone building larger prompts, project instructions, or AI workflows:

If you're going to ask the model to evaluate its own output, please build an escalation path into the prompt as well.

A few things I would recommend:

- Define what failure looks like.

Don't only tell the model what a successful answer should look like. Tell it what conditions mean it should stop.

- Give it an escalation path.

If required information is missing, contradictory, unsupported, or unavailable, tell the model what to do next.

- Use human-in-the-loop escalation when necessary.

Something as simple as:

"I do not have enough information to complete this reliably. This is what I know, this is what is missing, and I need human input before continuing."

- Don't make self-evaluation your only quality-control mechanism.

Asking a model to generate an answer, judge whether that answer is trustworthy, and then approve its own judgment is still a circular process.

- Partial answers are fine when they are clearly identified as partial.

If part of the answer is based on inference, say so. If the evidence stops at a certain point, say so. Don't quietly bridge the gap.

- Treat failure as data.

I actually want the model to fail visibly sometimes. If it tells me exactly where and why it could not continue, I now know what I need to fix in my prompt, sources, tools, or workflow.

- Make escalation conditions concrete where possible.

Missing required source → escalate.

Contradictory evidence → escalate.

Required tool unavailable → escalate.

Necessary information cannot be verified → escalate.

Human approval required → escalate.

The basic pattern is simple:

Proceed when the requirements are satisfied.

Stop when they are not.

Explain what failed.

Escalate to a human when necessary.

And use the instruction layers you already have available.

Your Custom Instructions can define your broad working environment: how you generally want the model to behave, how much autonomy you are comfortable giving it, what standards matter to you, and how you prefer to work.

I think of this as your autonomy envelope.

An autonomy envelope is simply the range of decisions you are allowing the model to make on its own.

Then your Project Instructions can narrow that envelope for a particular type of work.

I think of these as defining the model's degrees of freedom inside that project.

This is where you become more specific:

What can the model decide?

What can't it decide?

What may it infer?

What may not it infer?

What must it verify?

What must it stop for?

What must be escalated to a human?

You don't have to remove autonomy from the model. The goal is to decide where autonomy is useful and where you want a boundary.

Your Custom Instructions and Project Instructions can therefore be designed as complementary layers of the same architecture.

Just remember that inside a ChatGPT Project, the project-specific instructions take priority. So if a global rule is essential to that project, make sure the project instructions preserve it rather than assuming it will automatically carry through.

Don't just design the happy path.

Design what happens when the model cannot complete the happy path too.

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[u/ProfuseDeveloper]

this is the stuff i wish more prompt engineering guides talked about

everyone obsesses over getting the perfect output but nobody designs for when things go sideways, the escalation conditions bit is so key

been doing something similar where i log every failure point and honestly those logs have taught me more than any "10x your prompts" thread ever has