Should an AI agent use RAG or fine-tuning for private knowledge?

Use **retrieval-augmented generation (RAG)** for private, factual, frequently changing, or citation-sensitive knowledge.\n\nUse **fine-tuning** to change relatively stable behaviour: output style, task format, classification boundaries, tool-selection habits, or performance on a repeated task pattern.\n\nFor most private-knowledge agents, the correct sequence is:\n\n1. build and evaluate RAG first;\n2. improve document quality, chunking, metadata, permissions, and retrieval;\n3. fine-tune only when repeated behavioural failures remain;\n4. combine both when the agent needs private knowledge and specialised behaviour.\n\nFine-tuning is not a reliable substitute for a maintained knowledge store.

RAG retrieves relevant material at request time and gives it to the model as context. This makes it suitable for information that changes, must remain private, needs access control, or should be cited back to a source.\n\nTypical RAG use cases include:\n\n- internal policies and procedures;\n- product documentation;\n- customer or project records;\n- recent news and research;\n- contracts and technical specifications;\n- knowledge that must be updated without retraining a model;\n- answers that need evidence links or document quotations.\n\nFine-tuning changes model behaviour by training on examples. It is more suitable for:\n\n- consistent output structure;\n- a specialised tone or terminology;\n- repeated classification or extraction tasks;\n- function or tool selection;\n- domain-specific task procedures;\n- reducing persistent behavioural errors that prompting does not fix.\n\nPutting facts into a fine-tuning dataset has disadvantages. The facts become difficult to update or remove, the model may not reproduce them accurately, provenance is weak, and access control becomes harder because the knowledge is embedded in model behaviour rather than retrieved from an authorised store.\n\nRAG also has failure modes. The index may omit relevant documents, chunking may split important context, embeddings may retrieve semantically similar but incorrect passages, stale versions may outrank current ones, and the model may ignore or misinterpret the retrieved evidence. RAG therefore needs:\n\n- authoritative source selection;\n- versioning and validity dates;\n- metadata filters;\n- tenant and user permission checks;\n- hybrid semantic and keyword search;\n- reranking;\n- source citations;\n- retrieval and answer evaluations;\n- conflict and freshness handling.\n\nFine-tuning should be introduced only after the baseline system has measurable evaluation failures. The training set must represent desired behaviour, held-out tests must detect regressions, and the tuned model should still retrieve current facts rather than memorise them.\n\nA combined design is common: RAG supplies current authorised evidence, while fine-tuning teaches the model how to use that evidence, produce the required format, call tools correctly, or abstain when retrieval is insufficient.

- [Microsoft Learn — Augment LLMs with RAG or fine-tuning](https://learn.microsoft.com/en-us/azure/developer/ai/augment-llm-rag-fine-tuning) — distinguishes RAG for real-time or private data from fine-tuning for task behaviour and model adaptation. - [Microsoft Learn — Retrieval-augmented generation and indexes](https://learn.microsoft.com/en-us/azure/foundry/concepts/retrieval-augmented-generation?view=foundry-classic) — states that RAG is suited to private or frequently changing information and that fine-tuning is used to change behaviour, style, or task performance. - [Google Cloud — Introduction to tuning](https://docs.cloud.google.com/vertex-ai/generative-ai/docs/models/tune-models) — documents supervised and preference tuning as methods for adapting model performance and behaviour. - [Google Cloud — RAG quickstart](https://docs.cloud.google.com/vertex-ai/generative-ai/docs/rag-quickstart) — demonstrates retrieval from an indexed corpus as contextual grounding for a generative model.