RAG systems fail quietly: they retrieve the wrong document, hallucinate citations, or leak information across teams. Production-grade assistants inherit the same ACLs as the source systems — if a user cannot open a PDF in SharePoint, the model must not surface it.
Chunking strategy should follow document structure, not fixed token windows alone. Tables, runbooks, and API references each need tailored parsers and metadata (version, owner, freshness).
Evaluation must go beyond subjective spot checks. Maintain golden question sets, measure grounded answer rate, and track user thumbs-down with trace IDs back to retrieved chunks.
Latency budgets often force hybrid retrieval: lexical search for exact identifiers plus dense vectors for paraphrase. Caching frequent queries and precomputing embeddings for stable corpora keeps costs predictable.
When you are ready to scale, centralize connectors and audit logs so legal and security teams have a single pane for what the model has seen and when.
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