RAG vs fine-tuning: when to use each approach

You have documents. You want AI to answer questions about them accurately. Should you use RAG or fine-tune a model? This is the most common question we get from CTOs evaluating AI approaches. Here is the answer.

RAG in 30 Seconds

RAG retrieves relevant documents at query time and injects them into the prompt. The model generates answers based on the retrieved context. Your data stays in your database. The model uses it but does not memorize it.

Fine-Tuning in 30 Seconds

Fine-tuning trains a model on your data, baking the knowledge into the model weights. The model learns your domain vocabulary, style, and patterns. The data becomes part of the model itself.

When to Use RAG

Your data changes frequently. Product catalogs, documentation, knowledge bases, support articles — anything that updates regularly. RAG always retrieves the latest version. A fine-tuned model is stuck with whatever it learned during training.

You need citations. RAG can point to the exact document and paragraph that supports its answer. Fine-tuned models cannot — the knowledge is distributed across billions of weights with no traceability.

You need accuracy over style. For factual Q&A, data extraction, and search, RAG wins. The model does not need to memorize anything — it just reads and synthesizes.

Your budget is limited. RAG requires no training compute. You pay for an embedding model (cheap) and inference (per query). Fine-tuning requires GPU hours and ongoing retraining.

You need it fast. A RAG system can be production-ready in 2-4 weeks. Fine-tuning takes weeks of data preparation, training, and evaluation.

When to Fine-Tune

You need a specific voice or style. If your AI needs to write like your brand, follow strict formatting rules, or match a domain-specific tone, fine-tuning teaches the model your style.

You have a narrow, stable domain. Medical terminology, legal language, financial jargon — if the vocabulary is specialized and does not change often, fine-tuning helps the model understand your domain natively.

Latency is critical. Fine-tuned models do not need the retrieval step. No embedding, no vector search, no context assembly. The answer comes directly from the model. This saves 200-500ms per query.

You have abundant training data. Fine-tuning needs thousands of high-quality examples. If you have them, great. If not, you are fine-tuning on noise.

Our Recommendation: Start with RAG

For 90% of enterprise use cases, RAG is the right starting point:

• Faster to deploy (weeks vs months)
• Cheaper to run (no training compute)
• Always up-to-date (retrieval, not memorization)
• Traceable (citations to source documents)
• Easier to debug (you can see what the model was given)

Fine-tune only when RAG is not enough — when you need style adaptation, domain-native understanding, or when the retrieval overhead is unacceptable.

The best systems combine both: a fine-tuned model that understands your domain, augmented with RAG for current data and citations.

Decision Matrix