RAG vs. fine-tuning boundary first: Frequently updated knowledge and enterprise docs go to RAG. Fixed style/format tasks go to fine-tuning. Most customer service is RAG-first with light fine-tuning as backup. Explaining this tradeoff matters more than stacking solutions.
Retrieval quality is the bottleneck: Chunk by semantic paragraphs, keep heading hierarchy, hybrid retrieval (keyword + vector), reranker on top. Build the retrieval eval set first - without it you cannot tune the pipeline.
Hallucination control: Force citations in answers, refuse or hand off to humans when there is no evidence, and route critical actions (refunds, order changes) through deterministic tool calls instead of free-form generation.
Evaluation: Layered eval - retrieval recall, answer faithfulness, end-to-end task resolution rate, plus human sampling. Online, watch handoff rate and first-contact resolution, not BLEU.
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