RAG vs. Fine-Tuning: Choosing the Right Approach for Your AI Use Case

Two of the most common questions we get from clients exploring AI: "Should we fine-tune a model on our data?" and "What is RAG and do we need it?" They solve different problems, cost different amounts to build and maintain, and suit different use cases. Here is the decision framework we use.

What RAG Is and What It Is Good For

Retrieval-Augmented Generation (RAG) means the AI retrieves relevant documents from your knowledge base at query time and includes them in the context it uses to answer. The base model does not change — you are giving it access to your documents dynamically.

RAG is the right choice when: your knowledge base changes frequently (product catalogues, policies, documentation); you need the model to cite sources; you cannot afford the compute and time cost of retraining; your use case requires grounding the model in specific, authoritative documents.

What Fine-Tuning Is and What It Is Good For

Fine-tuning means training the base model on your data, updating its weights so it learns specific patterns, formats, or domain knowledge. The model itself changes. Fine-tuning is appropriate when: you need the model to respond in a very specific style or format consistently; you have thousands of high-quality examples of the correct behaviour; your use case does not depend on up-to-date external information; RAG latency is unacceptable for your application.

The Decision Matrix

• Knowledge changes frequently → RAG
• Need to cite sources → RAG
• Specific response format/style required → Fine-tuning
• Domain vocabulary is unusual → Fine-tuning or both
• Cost-sensitive, needs to go live quickly → RAG (faster to build and iterate)
• Highest possible quality on a narrow task → Fine-tuning (more investment, better ceiling)

Most enterprise use cases we see are best served by RAG first. If quality is still insufficient after optimising the retrieval system and prompt engineering, then fine-tuning on top of a RAG-grounded architecture is the path forward.