Two approaches, two philosophies. One searches your documents in real time, the other internalises your business knowledge. We untangle everything with concrete examples.
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.
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 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.
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).
We test 15+ available models on your real use case and recommend what works, not what gets the most press coverage. RAG, fine-tuning, hybrid: the decision comes from the data.
In 30 minutes, we analyse your case and tell you exactly which AI architecture best fits your data. Free.
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