AI hallucination — generating plausible-sounding but false information — is almost always an instruction problem, not a model problem. The same AI that fabricates citations for a vague request will give accurate, verifiable output when the instructions include the right constraints. Here is exactly what to add.
Diagnose your prompt free — 10 runs includedInclude this in your prompt: "If you are uncertain about any claim, say so explicitly. Do not present uncertain information as fact." This single addition causes most AI models to flag rather than fabricate.
When the AI should only use information you've provided, say so: "Use only the information in the context above. Do not add facts, statistics, or citations not present in the provided text."
Narrow what the AI is allowed to address: "Respond only about [specific topic]. If the question falls outside this scope, say so." Scope limits reduce the space where hallucination can occur.
Ask AI to mark unverified claims: "Mark any statement you cannot verify from the provided context with [unverified]." This makes hallucinations visible rather than hidden in confident prose.
Add three things: an explicit uncertainty instruction, a grounding constraint, and a confidence marker for unverified claims. These additions work across all major models and reduce hallucination significantly without affecting output quality on things the AI does know.
Without grounding instructions, AI fills knowledge gaps with statistically plausible but fabricated details — including fake citations that look exactly like real ones. Fixing it requires an explicit constraint: "Only cite sources provided in the context."
Partially. Newer models hallucinate less on well-known facts, but hallucination from underspecified instructions is an instruction design problem. A well-written prompt reduces hallucination more than a model upgrade alone.
Workflow Doctor checks your instructions for missing grounding constraints, absent uncertainty handling, and open scope limits — then repairs them. You get a corrected prompt with an explanation of what changed and why.