Generative AI · Apr 28, 2026 · 9 min read
RAG vs. fine-tuning for enterprise knowledge
A practical decision framework for grounding Claude in proprietary enterprise data.
Most enterprise Generative AI use cases do not need fine-tuning. They need retrieval — fresh, permissioned, well-chunked retrieval — feeding a strong base model like Claude Sonnet.
RAG wins when the source data changes weekly, when answers must cite primary sources, and when access control matters at the row level. Fine-tuning wins for tone, structured output formats, and narrow classification tasks where prompts get unwieldy.
In production we lean heavily on hybrid retrieval (dense + lexical), rerankers, and aggressive evaluation harnesses. The model is rarely the bottleneck; retrieval quality is.
Start with RAG, instrument everything, and only reach for fine-tuning when the evals tell you to.
