What is the difference between RAG and fine-tuning?

RAG connects the model to your up-to-date documents, with no retraining; fine-tuning adapts the model itself. RAG is often the most pragmatic place to start.

Agent or copilot: which should you choose?

A copilot assists a person inside their tool; an agent runs a sequence of steps end to end. The choice depends on the workflow in question.

Do you need a large model to get started?

Not necessarily: a suitable model, well integrated into the workflow, is worth more than a powerful model used badly.

Enterprise AI, explained without jargon

This glossary defines, in plain business language, the terms that come up in an AI project. Each definition fits in two sentences and says what the term is actually for. Use it as a reference when reading our solution pages and when making informed decisions.

The essential terms

RAG (Retrieval-Augmented Generation). A technique that connects a language model to your own documents so that it answers from your internal knowledge, with sources to back it up, rather than from generic knowledge. Useful for a business assistant or a searchable knowledge base.

AI agent. A system able to reason, plan and carry out a sequence of actions towards a goal, beyond simply answering. Useful for automating a complete workflow, with human sign-off at sensitive points.

Copilot. An AI assistant built into an existing tool that augments an employee (it suggests, drafts, summarises) without changing the way they work.

LLM (large language model). A model trained to understand and generate text. It is the engine behind most uses of generative AI in business.

LLMOps, industrialisation. Everything that keeps an AI solution running over time: evaluation, monitoring, governance, updates. It is what separates a POC from a solution in production.

Fine-tuning. Adapting a model to your data or to a specific task. Often a lower priority than RAG for a first project, because it is heavier to set up.

POC, pilot, MVP, production. A POC proves technical feasibility; a pilot tests it on a limited real scope; an MVP delivers the minimum useful value; production means value that is sustained, measured and adopted. Most projects fail somewhere between the POC and production.

Sovereign AI. A solution whose data, processing and code stay under your control, on hosting you control (in France or on-premise), rather than handed to an opaque third-party service. A prerequisite in regulated sectors.

Baseline. The quantified measure of the situation before a project (time, costs). Without it, there is no way to prove the gain, and therefore no way to measure the ROI.

Hallucination. A plausible but false answer produced by a model. RAG and continuous evaluation greatly reduce this risk by anchoring answers in your sources.

Frequently asked questions

What is the difference between RAG and fine-tuning?: RAG connects the model to your up-to-date documents, with no retraining; fine-tuning adapts the model itself. RAG is often the most pragmatic place to start.
Agent or copilot: which should you choose?: A copilot assists a person inside their tool; an agent runs a sequence of steps end to end. The choice depends on the workflow in question.
Do you need a large model to get started?: Not necessarily: a suitable model, well integrated into the workflow, is worth more than a powerful model used badly.