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Prompt 6: Scope Lock (Kill Hallucinations at the Source) Claude loves to wander. Lock it down immediately: “Answer strictly within [exact scope/background]. If something is outside this scope, say ‘Out of scope’ and stop. I prefer knowing what you don’t know over confident speculation.” This single line dramatically reduces confident bullshit.
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A single prompt instruction that forces Claude (or any LLM) to stay within a defined knowledge boundary instead of confidently speculating.
Prompt
Answer strictly within [exact scope/background].
If something is outside this scope, say 'Out of scope' and stop.
I prefer knowing what you don't know over confident speculation.Why it works
LLMs are trained to be helpful and produce fluent, confident-sounding answers — even when they lack reliable information. Without explicit constraints, the model will interpolate, guess, or confabulate to fill gaps. By defining a hard boundary and giving the model a pre-approved exit phrase ('Out of scope'), you remove the social pressure to always produce an answer.
The phrase 'I prefer knowing what you don't know over confident speculation' reframes the reward signal from the model's perspective. It signals that admitting ignorance is the correct, helpful behavior — not a failure. This aligns the model's output strategy with your actual needs.
The combination of a positive instruction (stay within scope), a specific fallback phrase (Out of scope + stop), and an explicit preference statement creates a three-layer constraint. Each layer catches a different failure mode: wandering, hedging, and over-explaining uncertainty.
When to use
- •When querying an LLM about a specific document, codebase, or dataset you've provided as context
- •When you need factual accuracy over completeness and can't afford confident wrong answers
- •When building a scoped assistant or chatbot that should only answer within a defined domain
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