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mediumAI Engineering

How do you reduce hallucinations in a production LLM application?

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01

Understand the problem

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

hallucinationgroundingcitationsreliability
02

Attempt it yourself

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

Nudge consolestandby

Stuck? Beam a request up — the console returns a conceptual nudge that guides your logic without spoiling the implementation.

03

Study the solution

Layer defenses: ground answers in retrieved or provided context and instruct the model to answer only from it; give an explicit out ('say you do not know if the context is insufficient'); require citations to source passages so claims are checkable; lower temperature for factual tasks; and verify high-stakes outputs do

Solution ready — 2 min read

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04

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 };
}
05

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