One of the most frequent questions we get from clients building AI products is: "Should we fine-tune a model or use RAG?" The answer, like most things in AI, is "it depends" — but there's a clear decision framework that makes the right choice obvious most of the time.
What RAG Does Well
Retrieval-Augmented Generation (RAG) adds a retrieval step before inference: given a user query, fetch the most relevant documents from a knowledge base, then pass those documents as context to the LLM. The LLM answers based on what it retrieved, not just what it was trained on.
RAG excels when your data changes frequently, when accuracy is paramount and you need to cite sources, when you have a large corpus of proprietary documents, and when you want to avoid the cost and complexity of training runs.
What Fine-Tuning Does Well
Fine-tuning adjusts the weights of a pre-trained model on your specific data. The model learns your style, domain vocabulary, output format, and reasoning patterns. Fine-tuning excels when you need consistent tone and format, when your task is highly specialised, when inference latency matters, and when your training data is stable.
The Decision Framework
Use RAG when the answer lives in your documents. Use fine-tuning when the answer lives in your format, style, or domain reasoning. Use both when you need domain-specific reasoning over a large, dynamic knowledge base.
The most powerful enterprise AI systems we've built combine both: a fine-tuned model that understands the domain and speaks the right language, augmented by RAG over a well-maintained knowledge base. The synergy is greater than either approach alone.
