How do you explain LLM hallucination mitigation to interviewers?

Short answer: Explain hallucination mitigation using a structured multi-layered engineering framework: grounding via Retrieval-Augmented Generation (RAG), prompt-level constraints (explicit system instructions and XML context tagging), decoding control (low temperature), and automated output evaluation (faithfulness checks and guardrails).

Detail

Interviewers look for candidates who understand that hallucinations cannot be eliminated by a single prompt tweak; mitigation requires a multi-tier pipeline.

                  ┌──► 1. Grounding Layer: RAG (Hybrid Search + Reranker)
                  ├──► 2. Prompt Layer: System Rules + Strict Context Tagging
Hallucination ────┼──► 3. Sampling Layer: Low Temperature (T=0.0 - 0.2)
Mitigation        ├──► 4. Output Guardrails: Pydantic Schema + Llama Guard
                  └──► 5. Evaluation Layer: Automated Faithfulness Evals

Communication Structure for Interviews

1. Root Cause Acknowledgment: Acknowledge that LLMs predict tokens probabilistically rather than retrieving database facts.
2. Grounding (RAG): Explain retrieving verified domain snippets to provide context.
3. Guardrails & Evals: Describe verifying output faithfulness programmatically before serving end users.

Example

Structured response framework summary table to use in interviews:

| Layer | Technique Applied | Impact on Hallucination |
| --- | --- | --- |
| **Retrieval** | Hybrid Search (Qdrant + BM25) + Reranker | Guarantees top context snippets contain factual ground truth. |
| **Prompting** | "Answer using ONLY provided context; if unsure say 'I don't know'." | Reduces speculation on out-of-domain questions. |
| **Decoding** | Set `temperature=0.0` | Eliminates random low-probability tail token sampling. |
| **Verification** | LLM-as-a-Judge Faithfulness Check | Catches ungrounded claims before returning response. |

Interview tips

• Avoid absolutes like "I solved hallucinations". Quote a measured before/after instead — "faithfulness on our 200-case eval set went from 0.88 to 0.97, and unsupported-claim rate from 12% to under 1%" — and name the eval set, its size, and who labelled it. Use your own numbers: an interviewer's next question is always "how did you measure that?", and a borrowed figure collapses immediately.
• Be ready to explain Ragas faithfulness evaluation metrics, including that the judge itself needs validating against human labels before you trust its numbers.

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