What each approach actually changes
RAG keeps the base model frozen and injects relevant document chunks at query time. Answers can cite sources and update when you upload a new PDF — no retraining cycle. Fine-tuning adjusts model weights on your examples, which can improve tone or task format but does not magically ingest a 500-page policy library.
Teams often conflate "custom AI" with fine-tuning. In practice, most Indian enterprise pilots we see — HR policy bots, broker brochures, support macros — ship faster and safer with RAG plus evaluation, not a custom weight checkpoint.
When fine-tuning is worth the cost
Consider fine-tuning (or preference tuning) when you need consistent output structure — JSON extraction, classification labels, or brand voice — and you have thousands of high-quality labeled examples. It also helps when retrieval noise is unavoidable and the task is narrow.
Fine-tuning does not replace governance. You still need versioning, PII handling, and regression tests. Budget for GPU time, data cleaning, and re-runs when the base model provider deprecates your snapshot.
A practical default for 2026
Default to RAG with a strong chunking and eval pipeline. Add fine-tuning only after you measure a specific gap — format adherence, domain jargon, or latency — that prompting and retrieval cannot close. Many Sabrixa engagements never need fine-tuning because grounded retrieval on customer documents solves the business problem.
Document the decision. Stakeholders remember "we trained our own model" even when retrieval would have shipped in four weeks. A written architecture note prevents expensive vanity training runs.