RAG or Fine-Tuning: Which AI Approach for Your Business?

Two approaches, two philosophies. One searches your documents in real time, the other internalises your business knowledge. We untangle everything with concrete examples.

The starting problem

An LLM (GPT, Claude, Mistral…) is trained on public data. It doesn't know your contracts, procedures or business jargon. To make it useful in your context, two main options exist.

RAG: the AI that searches before answering

With RAG (Retrieval Augmented Generation), your AI queries a document base for each question. It vectorises your content (contracts, product sheets, manuals), retrieves the most relevant passages and formulates a response with cited sources.

The key advantage: if a contract changes, you update the base, not the model.

Fine-Tuning: the AI that has internalised your knowledge

Fine-tuning consists of partially retraining the model on your data. The AI absorbs your tone, vocabulary and reasoning patterns. Result: it responds "like you" without needing context provided each time.

The hybrid approach: best of both worlds

In ~40% of our projects, we recommend a hybrid approach: a fine-tuned model for style and format, combined with RAG for fresh data. Example: a law firm with an assistant that writes briefs in the exact firm style (fine-tuning), drawing on the most recent case law (RAG updated daily).