Hallucination Mitigation

Best Practice: Hallucination Mitigation

A hallucination is when a large language model produces output that is fluent and confident but factually wrong or unsupported. Hallucination mitigation is the set of techniques that reduce this and make remaining errors detectable. Models generate plausible text, not verified truth, so without grounding they will sometimes invent facts, citations, or APIs. Mitigation matters because a single confident falsehood can mislead a user, break automation, or create legal and reputational harm; trustworthy AI depends on keeping output anchored to real sources.

It helps to remember what a language model actually does: it predicts likely text, not verified truth. Left unconstrained, it will sometimes produce a confident, fluent statement that is simply false, including invented citations, statistics, or API methods. Mitigation works on two fronts. First, reduce the rate of hallucination by grounding answers in retrieved sources, lowering temperature for factual tasks, and explicitly permitting the model to admit uncertainty. Second, make the errors that remain detectable through required citations, a verification pass that checks claims against sources, and faithfulness metrics tracked over time.

Step-by-Step Implementation Guidance

1. Ground answers in retrieved sources (RAG) and instruct the model to use only them.
2. Require citations so claims can be traced and verified.
3. Allow the model to say "I don't know" rather than guessing.
4. Lower temperature for factual tasks to reduce invention.
5. Add a verification step: a second pass checks claims against sources.
6. Constrain outputs to known values (enums, schemas) where possible.
7. Measure faithfulness with evals and track it over time.
8. Surface confidence and sources to the user for high-stakes answers.

For high-stakes answers, design the interface to support verification rather than hide it. Surfacing sources and confidence lets a user check a claim quickly, and constraining outputs to known values, such as enumerations or schema-validated fields, removes whole classes of invented answers. Faithfulness evals, which measure whether each claim in an answer is supported by the retrieved context, give a number you can track release over release, turning hallucination from an anecdote into a metric the team can manage down.

Common Mistakes Teams Make When Ignoring This Practice

• Treating fluent output as if it were verified fact.
• No grounding, so the model invents facts and citations.
• Punishing "I don't know," which pushes the model to guess.
• No verification pass for high-stakes answers.
• Never measuring faithfulness, so hallucination rate is unknown.

Tools and Techniques That Support This Practice

• Techniques: RAG grounding, citation, self-verification, lower temperature.
• Evaluation: RAGAS faithfulness, groundedness checks, LLM-as-judge.
• Patterns: retrieval with reranking, schema-constrained output.

How This Practice Applies to Different Migration Types

• Cloud Migration: Ground a migration assistant in real runbooks so it cannot invent steps.
• Database Migration: Require citations to schema docs for any mapping claim.
• SaaS Migration: Anchor integration guidance to actual vendor API docs.
• Codebase Migration: Verify that referenced functions and APIs truly exist before trusting output.

Checklist

• Answers grounded in retrieved sources
• Citations required and traceable
• Model allowed to say it does not know
• Temperature tuned for factual tasks
• Verification pass for high-stakes output
• Outputs constrained to known values where possible
• Faithfulness measured with evals