RAG vs Fine-Tuning: When to Use Which

"Should we use RAG or fine-tune a model?" is one of the most common questions we get — and the honest answer is that they solve different problems. Choosing wrong wastes months. Here's how we decide.

What each one actually does

Retrieval-Augmented Generation (RAG) keeps the model fixed and feeds it relevant information at query time. You store your knowledge in a vector database, retrieve the most relevant chunks for each question, and put them in the prompt. The model reasons over facts it's handed.

Fine-tuning changes the model itself by training it further on your examples. It adjusts the model's weights to shift its behavior, format, or style.

The mental model: RAG gives the model knowledge; fine-tuning gives the model skills.

Use RAG when…

• Your knowledge changes. Docs, policies, catalogs, tickets — anything that updates. With RAG you just update the index; no retraining.
• You need citations. RAG can point to the source document, which matters for trust, compliance, and reducing hallucinations.
• Access control matters. You can filter retrieval by user permissions so people only see what they're allowed to.
• You're starting out. RAG is faster to build, cheaper to iterate, and easier to debug — you can inspect exactly what was retrieved.

The majority of "chat with our knowledge base" and "answer questions about our product" problems are RAG problems.

Use fine-tuning when…

• You need a consistent format or style the base model won't reliably follow through prompting alone — a specific tone, a strict output structure, a domain's phrasing.
• You're teaching a behavior, not facts — classifying in a nuanced taxonomy, following a complex multi-step procedure the same way every time.
• Latency and cost matter at scale — a smaller fine-tuned model can match a larger general model on a narrow task, for a fraction of the per-call cost.

Fine-tuning is the right tool when prompting has plateaued and the gap is about how the model behaves, not what it knows.

They're not mutually exclusive

The strongest systems often use both: fine-tune a smaller model to handle your task and format reliably, then use RAG to feed it current, authoritative facts at query time. You get consistent behavior and up-to-date knowledge.

Before you reach for either, try prompting

Many problems blamed on "the model isn't good enough" are actually prompt and context problems. Before investing in a fine-tuning pipeline or a vector database, see how far you get with a well-structured prompt, good examples, and the larger model. It's the cheapest experiment you can run, and it tells you which of the two real solutions you actually need.

A quick decision guide

• Knowledge that changes, needs citations, or is permissioned → RAG.
• Consistent format/behavior the base model won't follow, or cost/latency at scale → fine-tuning.
• Both → fine-tuned task model + RAG for facts.
• Not sure yet → prompt-engineer first, then measure.

The right choice depends on your data, your latency budget, and how often your knowledge changes — and getting it wrong is expensive. If you're weighing this for a real product, tell us what you're building and a senior engineer will give you a straight answer on which path fits.