Start by grounding the model
The most powerful way to cut hallucinations is to stop relying on the model’s memory and instead feed it real, relevant text. Retrieval-augmented generation (RAG) searches a document store, pulls the most relevant chunks, and places them in the prompt, so the model summarizes verifiable material rather than inventing it. The instruction matters too: tell the model to answer only from the provided context and to say it does not know when the context is silent. Grounding addresses the root cause — missing or fuzzy knowledge — which is why it removes more hallucinations than any prompt trick alone.
Demand citations you can check
Ask the model to attach a source to every factual claim, ideally quoting or referencing the specific passage it relied on. This pushes the model toward statements it can actually support and gives you an audit trail. When citations are tied to retrieved documents, you can programmatically verify that each one exists in the supplied sources and reject answers whose references cannot be matched. Unverifiable prose becomes checkable claims, and the model learns, within the prompt, that fabrication will be caught.
Use verification and self-consistency
Two techniques attack hallucinations after the first draft. Chain-of-thought verification asks the model to reason step by step and then check its own answer against the evidence, which surfaces contradictions that a single fluent pass would hide. Self-consistency generates the same answer several times and keeps what the runs agree on; stable facts recur, while hallucinations tend to vary, so disagreement is a built-in warning sign. Both cost extra compute, so apply them where accuracy is worth the price — legal, medical, financial, or factual lookups rather than casual brainstorming.
Tune settings and prompts sensibly
Smaller levers still help at the margin. Lowering temperature makes output more deterministic and slightly less prone to creative invention, useful for factual tasks. Clear, specific prompts reduce ambiguity that the model would otherwise fill with guesses. Explicitly permitting “I’m not sure” or “the document does not say” lowers the pressure to fabricate. Narrowing scope — one question at a time, with the exact format you expect — keeps the model focused. None of these fix the root cause, but together they tighten the gap.
Calibrate confidence and keep a human in the loop
Finally, design the workflow around the reality that no model is perfectly reliable. Ask for a confidence signal and route low-confidence answers to review. Surface the sources so a person can spot-check claims quickly. Reserve full automation for low-stakes tasks and keep human verification on anything consequential. The goal is not a model that never errs — that does not exist yet — but a system where errors are rare, visible, and caught before they cause harm. Stack these techniques and hallucinations move from a constant hazard to a manageable, well-bounded risk.