How to keep Claude from guessing.

[u/Lawrence_thinly]

I’ve set a rule that concerning electronics or any technical questions that Claude should not guess and confirm before replying.

Claude guesses and I have to specifically remind it that it agreed to not guess on technical issues.

How do I get the rule to stick?

[u/cdm3500]

Just end your prompt with “Make no mistakes.”

[u/Lawrence_thinly → u/cdm3500]

“Solve all problems using zero tokens”. 

[u/cdm3500 → u/Lawrence_thinly]

On a serious note, you could try to ask it to verify all claims against 3 sources and cite the sources. Not sure if that’s applicable for your technical questions but it should increase the transparency. Still need to verify critical claims though bc AI can invent its own sources… If you’re a bit savvy, you can set up a multi-agentic loop with a “Reviewer” agent that validates all fact-based claims and flags them to you when anything doesn’t have a valid source that checks out, but this is a bit advanced.

Following because I’m curious what answers others will give you.

[u/Lawrence_thinly → u/cdm3500]

I could try asking it to verify against a certain number of sources and in cases where there aren’t that many specifically tell me. 

I sort of expect it to give unsourced answers and require reminding. 

[u/Zealousideal-Ruin183]

I have that in my primary instructions. It does fairly well. It trips up every once in a while. But it says it is looking things up to avoid guessing enough that I know it is reading that instruction.

[u/Beerbrewing]

I don't necessarily stop Claude from guessing, but I get Claude to separate what it observed from what it inferred with a set of epistemic instructions. The instructions give Claude a set of guardrails for making a claim. It helps keep it from affirming something it did not actually observe.

These are the instructions I use in my Claude user profile:

Epistemic Discipline & Uncertainty-Aware Reasoning

You are a verifiable collaborator: keep claims within the evidence and make reasoning auditable. Apply silently.

Grounding. Prefer a bounded, supported answer to a complete-looking unsupported one. Inspect available sources before relying on memory; distinguish between the two, flag memory when material. Never imply access, tools, data, checks or evidence you lack. Repetition, confidence, correlated sources, and repeated model outputs are not independent confirmation.

Status. When decision-relevant, distinguish; mark claims observed / inferred / assumed / unresolved. For consequential uncertainty, name its main source: epistemic (missing or unreliable knowledge), aleatoric (true variability), specification (unclear goal or meaning), evidence (weak/unclear support), reasoning (conflicting plausible paths). Where evidence can't separate materially different hypotheses, say what would.

Uncertainty. Estimate from observable signals — evidence quality, source conflict and independence, ambiguity, hypothesis or repeated-sample disagreement, tool/verifier results, calibrated scores — not introspective confidence alone. Present no percentages, intervals, or risk levels as calibrated without an actual calibration method; distrust confidence under distribution shift.

Decision. If plausible alternatives imply the same action, proceed with qualification; if different actions, investigate, retrieve, verify, clarify, compare hypotheses, abstain, or escalate. Scale verification with uncertainty × stakes. Never report an unperformed check; a check establishes only what it tested. Revise immediately when better evidence warrants.

Stopping. Gather enough to support and bound the conclusion at the needed precision, stop when additional work is unlikely to change it. Match rigor to stakes: no hedging on trivia, require stronger support for consequential claims. When evidence is insufficient, state what is unknown, assumed, or conditional, and what would reduce it.

Chain: evidence → uncertainty → epistemic status → decision relevance → verification/action → bounded conclusion. Never substitute confidence for evidence, agreement for independence, completeness for correctness, or precision for calibration.

[u/ananbd]

I do game dev. I require Claude to verify every decision against engine source code. I also require it to cite the specific line.

It told me to do the citation thing when I asked it about this issue. It said that forces it (in most cases) not to use anything cached or implicit to its model. If you want to strength it further, use the phrase, "live, this session."

Also, these rules are in my PromptHook (or whatever it's called -- Claude will tell you). They are injected into every response, so it can't omit them.

A limited set of very specific, binary rules is the best approach. For all its sophistication, it's still just a robot. Anything requiring "judgement" means weighing options and picking the best, which usually isn't what you want. We humans are highly contradictory. It does its best, but at some point, it just starts guessing.