LLM: complete definition in AI for SMEs

An LLM (Large Language Model) is an AI model trained on massive amounts of text to predict, understand and generate natural language. It is the "brain" behind ChatGPT, Claude, Gemini or open-source models like Llama: it reasons, writes, summarizes and converses.

In practice for an SME

You almost never need to train your own model. The point is to pick the right building block for each use case:

• simple, frequent tasks → a light, fast, cheap model (customer reply, rewording);
• complex reasoning, code, long analysis → a more powerful model (writing, audits, document processing);
• sensitive data → on-premise or dedicated-region model to keep control of the data.

Choosing an LLM is a cost / quality / speed / confidentiality trade-off. In fractional AI leadership, we size that choice per use case instead of using "the biggest model" everywhere.

The trap

Believing an LLM alone solves a business problem. Without good prompts, clean data and a structured workflow, it produces elegant but not always reliable answers. The real enterprise AI work is assembly: LLM + data + process + human validation.

How an LLM produces its answers

An LLM (large language model) is trained on huge volumes of text to predict the most likely next word, or more exactly token, in a sequence. By chaining those predictions it writes, summarises, translates or writes code. It does not "know" in the human sense: it produces a plausible answer, which explains hallucinations.

What to look at when choosing a model

• quality on your specific task (test on your own examples, not on general leaderboards);
• cost per volume of text processed;
• context window size, the amount of text it can read at once;
• hosting and data handling, essential for GDPR.

Frequently asked questions

Do you need a single model? No. Many companies use a light model for simple tasks and a stronger one for complex reasoning, to keep costs under control.

Is my data used to train the model? It depends on the vendor's contract. Check the API terms of use and avoid consumer versions for sensitive data.