Sort common requests into prompting, retrieval, or fine-tuning based on what problem they actually solve.
Sort each request into the lever it most strongly suggests. Prompting first Retrieval first Fine-tuning candidate Make the model answer from the latest travel policy PDF and cite the clause it used. Teach the assistant to always produce risk, recommendation, and next step in that order across thousands of weekly summaries. Show the model three examples of the exact JSON shape we want before it extracts contract fields. Let the assistant inspect current pricing tables before it answers renewal questions. Keep the same terse customer-success voice on every stable QBR summary at scale. Clarify the audience, constraints, and desired tone…
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