RAG or fine-tuning for your AI system?
The decision rule in one line, the cost and freshness trade-offs, and why most production systems end up using both.
Use RAG when the facts change — documents, policies, catalogues, prices, inventory, internal wikis. Use fine-tuning when the behaviour needs to change: specialised jargon, a consistent output format, a house tone, or cutting inference cost at high volume. The test is simple. If the answer would be wrong next month because the underlying information moved, that is a RAG problem and training will not fix it. If the model knows the facts but says them badly, in the wrong format or too expensively, that is a fine-tuning problem and adding more retrieval will not fix it. Most mature systems use both: fine-tuning for how it speaks, retrieval for what it knows.
Side by side
| Factor | RAG (retrieval) | Fine-tuning |
|---|---|---|
| What it changes | What the model can look up | How the model behaves and phrases things |
| Updating facts | Re-index a document — minutes | Retrain — hours to days |
| Upfront cost | Lower; embedding and a vector store | Higher; data preparation, GPU time, evaluation |
| Cost per query | Higher — retrieved context makes prompts longer | Lower — shorter prompts, smaller model can suffice |
| Citing a source | Natural — you know which document answered | Hard — the knowledge is inside the weights |
| Handling jargon | Adequate | Strong — this is what training is for |
| Output format consistency | Prompt-dependent, can drift | Reliable once trained |
| Data needed | Your documents | Hundreds to thousands of quality examples |
Which one fits you
Choose rag (retrieval) if…
- Your answers depend on documents, policies or catalogues that change
- You need the system to cite where an answer came from
- You need it live in weeks, not months
- You do not have thousands of labelled examples
- Auditors or regulators need to see the source of an answer
Choose fine-tuning if…
- The model needs domain vocabulary it consistently gets wrong
- Output must follow a strict structure every time
- Query volume is high enough that prompt length is a real cost line
- You need a smaller, cheaper model to perform like a larger one
- Latency matters and you cannot afford a retrieval round trip
Where Skanda AI sits
We recommend RAG for most use cases and fine-tuning where volume or language specificity justifies it, and we will say which one your case needs before quoting. Fine-tuning is the more impressive-sounding option and the more frequently wasted one — a model trained on last quarter’s policy document is confidently wrong in a way a retrieval system simply is not. Where fine-tuning genuinely earns its cost is Indian-language handling and high-volume inference, and we run both. Timelines: RAG systems reach production in two to four weeks, fine-tuning programmes in eight to twelve.
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Frequently asked
Is fine-tuning better than RAG?
Neither is better; they solve different problems. RAG changes what the model can look up, fine-tuning changes how it behaves. If your answers go stale, that is RAG. If your answers are stale-proof but badly phrased, inconsistently formatted or too expensive to generate, that is fine-tuning. Choosing fine-tuning for a freshness problem is the most common and most expensive mistake in this decision.
Can you use RAG and fine-tuning together?
Yes, and it is the usual end state for a mature system. Fine-tune the model so it speaks your domain language and returns the structure you need, then attach retrieval so the facts it states are current and sourceable. The two are complementary — the training governs behaviour, the retrieval governs knowledge.
How much data do you need to fine-tune a model?
Realistically hundreds to a few thousand high-quality examples, and quality matters far more than count. A thousand carefully curated examples outperform ten thousand noisy ones. If you do not have that data yet, start with RAG — it works from the documents you already have, and the conversations it produces become the training set for a later fine-tune.
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Get a straight recommendation
Send us your situation and we will tell you which option we would pick in your position — and what it would cost. Our rates are negotiable and we will work to beat a competing quote.