Reducing LLM hallucinations: why layered system controls beat a single fix

A common AI engineering interview question asks how to reduce hallucinations, and the strongest answers treat it as a system-level problem rather than one tweak like lowering temperature. The recommended layers are grounding the model through retrieval-augmented generation with instructions to answer only from context, improving retrieval with chunking, hybrid search, reranking and tuned top-k, and constraining output via citations and schema validation. Further steps include verifying each claim against sources with an entailment or LLM judge, routing low-confidence answers to abstention or human escalation, and continuously measuring faithfulness and relevance with a golden dataset. The article warns against relying on temperature zero, which makes output deterministic but not more accurate, and against blaming the model before checking whether the correct passage was retrieved.