Evidence-first authority page
How to Reduce AI Hallucinations in Agents
Practical, evidence-based techniques for reducing hallucinated output in AI agents, and which ones actually address the cause versus the symptom.
Direct answer
Reduce hallucinations by grounding responses in retrieved source material (RAG), constraining output to structured formats the model can't easily fabricate around, and explicitly instructing the model to say 'unknown' rather than guess — no technique eliminates hallucination entirely, so pair these with a verification step for anything consequential.
Last reviewed 2026-08-29
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Grounding reduces the opportunity to hallucinate
A model asked to answer from its own training data has to reconstruct facts from memory, which is where hallucination originates. A model given the relevant source text directly and asked to answer from it has a much narrower job — summarize or extract from what's in front of it — which measurably reduces fabricated details, though it doesn't eliminate the risk of the model still misreading or extrapolating beyond the provided text.
Explicit permission to say 'unknown' matters more than it seems
Many hallucinations happen because the model is implicitly rewarded (by training) for producing a confident-sounding answer rather than admitting uncertainty. Explicitly instructing the model that 'I don't know' or 'the source doesn't say' are acceptable, correct answers measurably reduces fabrication on questions outside the provided context.
Structured output narrows the failure mode
Constraining a model to return a specific schema (a defined set of fields, an enum of allowed values) reduces the model's room to invent plausible-sounding but ungrounded prose — it's harder to hallucinate a fabricated date inside a strict `YYYY-MM-DD` field validated on the way out than inside a free-text paragraph.
What doesn't reliably fix it
Simply asking the model to 'be accurate' or 'don't make things up' in the prompt has limited, inconsistent effect on its own — it's a weak lever compared to actually changing what the model has access to (grounding) and how its output is checked (verification, structured constraints). Treat prompt-level pleading as a minor addition, not the primary defense.