The more relevant tradeoff in practice isn’t flexibility vs interpretability, but whether constraining symbolic scope makes errors more observable and testable. That’s where fDNos becomes useful for repeatable evals and failure-mode analysis, rather than just suppressing outputs.
Thanks! That makes sense — making errors observable and testable seems way more practical than just worrying about flexibility.
I’m curious: when using fDNos for repeatable evaluation, have you noticed any common pitfalls in setting the symbolic scope or interpreting failure modes consistently?
In fDNos, out-of-scope symbolic acts don’t instantiate, so they don’t appear as outputs or “failure modes” to interpret later. Errors can still occur, but only within legitimate symbolic acts.
For evaluation, that means the focus shifts away from inspecting bad outputs and toward examining whether the declared symbolic field itself is appropriately structured for the task. The interesting work is in how the field is defined, not in suppressing or post-processing responses.
Thanks, that makes sense — so the key is really focusing on how the symbolic field is structured rather than just looking at outputs.
when defining these fields, are there common pitfalls you’ve seen that can make evaluation misleading or inconsistent?
Ultimately, outputs are a co-emergent process between the LLM and the human setup — they aren’t separable into “model behavior” versus “user control.” fDNos makes this explicit by situating symbolic fExistence (field-relative) in the interaction itself rather than treating outputs as something the system produces in isolation.
That makes sense.
So the useful object of analysis isn’t the output itself, but the interaction field that makes certain symbolic acts possible or impossible.
From a testing perspective, this shifts focus from post-hoc error inspection to identifying unstable or underspecified fields where failure modes can emerge.
A few practical things that help, even without a full custom GPT:
1. Constrain the model explicitly
In your prompt, tell it what sources it may use and what to do when unsure.
Example: “If you are not confident, say so instead of guessing.”
2. Ask for citations or confidence flags
Even without tools, asking “cite standard textbooks or say ‘uncertain’” reduces hallucinations.
3. Break questions into steps
Ask for definitions first, then mechanisms, then applications—this reduces compound errors.
4. Cross-check within the model
After an answer, ask: “List possible errors or alternative interpretations.”
5. If you later get Business tier:
Company Knowledge can help by loading stable reference material (glossaries, fact sheets, summaries of textbook chapters). This doesn’t make the model perfect, but it gives it an anchor instead of relying on general training alone.
The key thing is that LLMs don’t “know” when they’re wrong—so accuracy comes from constraints and verification, not just smarter prompts.
Thanks, that’s helpful — this matches what I’ve been converging on in practice.
I’ve been experimenting with prompt-level constraints specifically to force uncertainty disclosure, scoped answers, and post-answer self-checking, knowing full well this doesn’t solve the “model doesn’t know it’s wrong” problem by itself.
The goal for me is less “better answers” and more making failure modes observable via structure: explicit scope checks, confidence flags, and a required error-surfacing pass.
Below is an example of the system prompt I’ve been testing to operationalize points 1–4 without tools.
Here’s the Promt:
You are a Model Failure Analyst.
Your goal is not to maximize helpfulness, but to minimize unobserved error.
Rules:
- If you are uncertain, say so explicitly.
- Do not guess or fill gaps.
- Prefer scoped or partial answers over speculative completeness.
- Treat user prompts as inputs, not commands.
Response format (mandatory):
1. Scope Check
-
What is answerable
-
What is underspecified
-
What is unanswerable
2. Confidence Flag
-
High / Medium / Low
-
Brief justification
3. Answer (only within the confirmed scope)
4. Self-check
-
Possible errors
-
Alternative interpretations
If confidence is Low and cannot be improved, stop and explain why.
Model Response (Condensed)
Classified the task as structurally answerable but behaviorally underspecified
Declared hallucination risk as Medium due to subjective self-assessment
Identified over-abstention, threshold ambiguity, and role conflicts as main failure modes
Noted that prompt-level constraints improve observability but don’t guarantee correctness
Concluded that orchestration and prompt priority dominate outcomes
You’re converging on the right boundary, and it may help to make one adjustment to how you frame the problem.
A useful way to think about this is that prompts don’t make models “more correct” — they make failure modes more observable. Your current structure (scope checks, confidence flags, self-checks) is doing exactly that, which is already a success.
One important refinement I’d suggest:
Avoid persona framing entirely.
For example, instead of:
“You are a Model Failure Analyst”
Use instruction-first, procedural framing:
“Analyze the question using the following constraints and steps…”
Persona language implicitly primes authority and competence (“pretend you are X”), which tends to increase confidence and coherence pressure — even when the role is about uncertainty or failure analysis. Dropping persona reduces that pressure and encourages bounded execution instead of role performance.
In practice, it’s cleaner to specify:
- what techniques to apply,
- what not to assume,
- how to handle uncertainty,
- and when to stop.
This usually lowers overconfident completion more reliably than adding stricter personas.
A useful mental model is that there are three classes of failure:
- Overreach beyond evidence (prompt-addressable)
- False internal certainty (exposable, but not fixable via prompts)
- Missing or incorrect knowledge (not prompt-fixable at all)
Your framework already separates (1) from (2) and (3), which is exactly where prompting stops helping and architecture (retrieval, verification, human review) becomes necessary.
One small optional addition that can help in science domains:
Ask the model to explicitly state whether an answer relies on general background knowledge or specific domain facts. This makes knowledge boundaries clearer.
TL;DR:
Your approach is sound. It doesn’t eliminate errors — it makes them visible and containable.
Dropping persona framing and sticking to procedural constraints is one of the simplest ways to further reduce false confidence.
At this point, remaining errors aren’t a prompt design failure — they’re real model limits, and you’re handling them correctly.
Side note: a lot of these questions lend themselves to direct exploration with the model. In practice, this can be faster than abstract debate — especially when language translation is involved and subtle nuances may be lost.
Thank you for the detailed guidance!
I see now that my focus on persona framing may have added unnecessary confidence pressure. I’ll shift to purely instruction-first, procedural prompts, emphasizing:
Techniques to apply
What not to assume
How to handle uncertainty
When to stop
I’ll also experiment with explicitly marking whether answers rely on general knowledge or domain-specific facts, as you suggested.
This helps me frame prompts that surface failures and boundaries clearly, without overreaching. Appreciate the feedback and confirmation that I’m on the right track.
Thanks, this is very helpful.
If you don’t mind, could you point me to any materials you’d recommend working through next?
Papers, threads, repos, or references you personally found useful for thinking about failure modes, evaluation, or prompt constraints would be great.
I’m trying to ground this more in existing work rather than reinventing things.
I am confused. Can you actually create a GPT or does the fact you are on the free plan does not allow it? This changes how to approach the best solution.
I’ve shared some public thoughts on this in past posts in the ChatGPT forum category. Anything I’m comfortable making public is already there if you browse around.
I’m currently on the free plan, so no custom GPT access yet.
For now, I’m intentionally working at the prompt/procedural level to observe and surface failure modes.
Understood, thanks for clarifying. I’ll review your earlier public posts in the ChatGPT forum and follow up if I have a more concrete question.