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Generative AI and LLMs: understanding large language models
September 4, 2026TECHNÉA CONCEPT
Large language models (LLMs) are at the heart of generative AI. They write, summarise, translate and answer — but behind these capabilities lie notions that are often poorly understood. Here is the essential, without jargon.
LLM, generative AI: what is the difference?
- An LLM (large language model) is a model trained on huge text corpora: GPT, Claude, Mistral, Qwen, Llama…
- Generative AI is the family of uses built on these models: generating text, images, code.
In other words: the LLM is the engine, generative AI is what you do with it.
Fine-tuning: refining a model
Fine-tuning consists of retraining an existing model on your specific data so that it masters your vocabulary and your edge cases. It is useful for very specific needs, but often unnecessary: a model connected to your documents (via RAG) is enough in most cases.
Fine-tuning or RAG?
| Criterion | Fine-tuning | RAG |
|---|---|---|
| Goal | specialise the model | ground answers in your documents |
| Cost | high (training) | low |
| Update | retraining required | add a document |
| Use case | very specific jargon | answers from your data |
The main models in 2026
- GPT (OpenAI): versatile, large ecosystem.
- Claude (Anthropic): strong writing and analysis.
- Mistral: French player, strong sovereignty option.
- Qwen (Alibaba): good performance/cost ratio, deployable locally.
Cloud or private: choose according to your data
- Cloud: simple, fast, ideal for non-sensitive uses.
- Private / on-premise: your data never leaves your company — essential for sensitive data, health, legal, or strong GDPR constraints.
Which profitable uses for an SMB?
- Answer recurring questions (internal and customers).
- Summarise and analyse documents.
- Draft documents (quotes, replies, reports).
- Classify and sort e-mails or requests.
Limits and points of caution
- Hallucinations: an LLM can produce plausible but false answers. Hence the interest of grounding it to your sources (RAG).
- Biases: models reflect their training data.
- Costs: large-scale usage has a cost, to be controlled according to volume and the chosen model.
Frequently asked questions
How much does using an LLM cost?
It depends on the model and the volume. An assistant connected to your documents can cost from a few tens to a few hundred euros per month. A private deployment has an initial infrastructure cost, but predictable usage costs.
How to choose between GPT, Claude, Mistral and Qwen?
According to your priorities: versatility (GPT), writing quality (Claude), French sovereignty (Mistral) or locally deployable cost/performance ratio (Qwen). The right choice depends on your use case and your data constraints.
Conclusion
Understanding LLMs lets you choose the right uses without over-investing. To identify what pays off for your business, discover our AI solutions and our Private & secure AI.