When companies come to me wanting to build AI products, one of the first questions is often: "Should we fine-tune a model on our data?"
My answer is almost always: "No. Start with RAG."
Here's why Retrieval Augmented Generation (RAG) is usually the better choice for enterprise AI projects, and the specific situations where fine-tuning makes sense.
What's the Difference?
Fine-tuning means taking a pre-trained model and training it further on your specific data. The model's weights are updated to "learn" your domain.
RAG means keeping the base model as-is, but giving it access to relevant information at query time through retrieval from a knowledge base.
Why RAG Wins for Most Use Cases
1. Time to Value
A RAG system can be built and deployed in days or weeks. Fine-tuning requires:
- Curating training data (often months of work)
- Training runs (expensive, time-consuming)
- Evaluation and iteration
- Deployment of custom models
For most projects, getting something useful in front of users quickly beats perfect accuracy later.
2. Updateability
Your company knowledge changes. New products launch. Policies update. Documentation evolves.
With RAG, you update your knowledge base and you're done. With fine-tuning, you need to retrain – which means maintaining training infrastructure, managing model versions, and accepting downtime.
3. Transparency
When a RAG system gives an answer, you can see exactly which documents it retrieved. This makes debugging straightforward and allows you to build citation features.
Fine-tuned models are black boxes. When they're wrong, figuring out why is much harder.
4. Cost
Fine-tuning requires significant compute for training. RAG requires compute for retrieval (cheap) and inference (same as any LLM call). For most organizations, RAG is dramatically cheaper.
5. Hallucination Control
Fine-tuned models can still hallucinate – they've just learned new patterns, not new facts. RAG systems are grounded in actual documents, making it easier to prevent and detect hallucinations.
When Fine-Tuning Makes Sense
RAG isn't always the answer. Consider fine-tuning when:
Style is critical: If you need the model to write in a very specific voice or format consistently, fine-tuning can help.
Latency matters: RAG adds retrieval latency. For real-time applications where every millisecond counts, a fine-tuned model might be faster.
The task is specialized: If you're building something like a code completion tool for a proprietary language, fine-tuning on that language's syntax is probably necessary.
You have abundant, high-quality training data: If you have thousands of examples of exactly what you want the model to do, fine-tuning can be effective.
The Practical Approach
Here's what I recommend to most clients:
- Start with RAG – Build a system using retrieval over your documents
- Evaluate thoroughly – Understand where it works and where it fails
- Optimize the retrieval – Most "RAG failures" are actually retrieval failures
- Consider hybrid approaches – Sometimes combining RAG with lightweight fine-tuning (like LoRA) gives the best results
- Fine-tune only if necessary – After you've exhausted RAG optimizations and have clear evidence that fine-tuning will solve your remaining problems
Key Takeaways
- RAG delivers faster time-to-value for most enterprise use cases
- It's easier to maintain, debug, and update
- Fine-tuning has its place, but shouldn't be the default choice
- Start simple, measure results, and add complexity only when needed
The best AI projects I've worked on started with the simplest approach that could possibly work. That's usually RAG.