What each one actually is
- RAG (Retrieval-Augmented Generation) — at query time, you fetch relevant documents from a vector store and inject them into the prompt. The model "knows" things by reading them in context.
- Fine-tuning — you take a base model and continue training it on your data, baking knowledge or behavior into the weights themselves.
When to use RAG
- Your knowledge changes frequently (docs, products, policies)
- You need citations / source attribution
- Different users need access to different subsets of data
- Your dataset is small (<10K examples)
- You need fast iteration — change the docs, not the model
This is the right answer for ~85% of use cases.
When to use fine-tuning
- You need a specific output format / style consistently
- You're doing high-volume classification where prompt cost matters
- Your task requires implicit reasoning patterns RAG can't surface
- You have 5K+ high-quality I/O pairs to train on
- You're running an open-source model and need it smaller / cheaper
When to use both
The 2026 production pattern is increasingly "RAG + light fine-tuning." Fine-tune on style + output format. Use RAG for knowledge. Best of both — and the marginal cost is small now that OpenAI / Anthropic / Together support cheap LoRA fine-tuning.
Cost & complexity comparison
| Dimension | RAG | Fine-tuning |
|---|---|---|
| Setup cost | $2K–$15K | $5K–$50K |
| Iteration speed | Hours | Days–weeks |
| Per-query cost | $0.001–$0.05 (incl embedding lookup) | Same as base model |
| Maintenance | Update the docs | Re-train on new data |
| Citation support | Native | Requires extra layer |
| Best at | Knowledge | Style + format |
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