• RAG — Retrieval-augmented generation — fetching relevant documents first and pasting them into the model's context so it answers from your data instead of memory.
• fine-tuning — Taking a trained model and training it a bit more on your own examples so it gets better at one specific job.
• hallucination — When a model states something false with full confidence — inventing facts, citations, or APIs that don't exist.

It's a real production account of the RAG-vs-fine-tuning tradeoff for an internal support copilot handling tens of thousands of questions a month, including the concrete hallucination-mitigation approach used.

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