mediumAI Engineering

How do you reduce hallucinations in a production LLM application?

Q: How do you reduce hallucinations in a production LLM application?

**TL;DR.** Layer defenses: **ground** answers in retrieved context, give the model an explicit **abstain** path, require **citations**, lower temperature for factual tasks, and **verify** high-stakes outputs downstream. Measure **faithfulness** in evals so regressions surface. retrieve evidence in ground answer or abstain <line class='d-

463 views

Understand the problem

Grounding, abstention, citations and verification — a layered anti-hallucination playbook.

hallucinationgroundingcitationsreliability

Attempt it yourself

Sketch your approach before reading the solution — that's what interviews test.

Stuck? AI Nudge Available

Get a conceptual hint to guide your logic without spoiling the final implementation.

Study the solution

The solution is waiting

Give it an honest attempt first — then compare your thinking with the full walkthrough.

Read the code

Citation check that rejects unsupported answers

const reply = await answerFromDocs(question, docs);   // prompt requires [doc-N] cites

const cited = [...reply.matchAll(/\[doc-(\d+)\]/g)].map((m) => Number(m[1]));
const valid = cited.length > 0 && cited.every((id) => docs.some((d) => d.id === id));

if (!valid) {
  metrics.increment("answer.rejected.no_valid_citations");
  return { text: "I could not verify an answer from the available documents.",
           escalate: true };
}

Join the discussion

Discussion (0)

No responses yet. Be the first to share what you think.

How do you choose between open-weight models and proprietary API models?

What is reranking and why add it after retrieval?