A LLM (large language model) is a neural network trained on massive text corpora to predict and generate language. ChatGPT, Claude, Mistral, and Llama all rely on this architecture. Getting the best out of these models is not limited to asking a question in a chat: the formulation of the prompt, the choice of model, and how to orchestrate multiple tools radically change the quality of the responses obtained.
Context window and conversation memory: the technical parameter to master
The context window refers to the amount of text that a LLM can process at one time, measured in tokens (fragments of words). Recent models accept increasingly larger windows, some exceeding a million tokens. This race for large contexts changes the way we work.
With a wide window, it becomes possible to inject an entire document (contract, report, knowledge base) into a single conversation and ask specific questions about it. The response becomes more relevant because the model has all the necessary information without intermediate summarization.
Two limitations persist. First, the longer the context, the higher the computational cost, and thus the price per request via API. Second, LLMs tend to exploit information located in the middle of a very long text less effectively. Placing the most relevant data at the beginning or end of the prompt significantly improves the quality of the response.
To delve deeper into optimal usage on Toujours Le Bon Choix, several strategies for structuring context are detailed according to common use cases.
Prompt engineering: structuring a request to obtain an actionable response
Prompt engineering is the discipline of formulating instructions in a way that precisely directs the model’s output. A vague prompt produces a generic response. A structured prompt yields an actionable result.
Three components of an effective prompt
- The role: assigning an identity to the model (“You are a lawyer specialized in French labor law”) frames the language register, level of detail, and references used.
- The output format: specifying whether the response should take the form of a table, a bullet list, a summary paragraph, or a block of code avoids unnecessary reformulations afterward.
- Explicit constraints: maximum length, language, prohibition on citing unverified sources, obligation to signal uncertainties. Each constraint reduces the space of possible responses and increases precision.
Adapting the prompt to the nature of the task makes a measurable difference. A prompt aimed at generating code benefits from including an example of expected input and output. A data analysis-oriented prompt works better when the data is presented in a tabular format directly within the text of the request.
Multi-LLM orchestration: why companies use multiple models
A study by Andreessen Horowitz conducted among 100 CIOs in 2025 shows that an increasing proportion of companies are using five or more models in production. The era of the single model is over: multi-model orchestration has become a field of engineering in its own right.
The principle is based on a separation between two layers. The control plane decides which model to call, with which parameters and in what order. The data plane ensures the traceability of prompts, injected contexts, and outputs for audit and compliance.
When to route to which model
A compact and fast model suffices for ticket classification or email sorting. A more powerful model takes over for long writing, legal analysis, or multi-step reasoning. This dynamic routing allows for reducing inference costs without sacrificing quality on complex tasks.
LLM agents push this logic further. An agent is a program that uses a LLM as a reasoning engine but can also call external tools (web search, calculations, databases). The agent decides the sequence of actions to execute to solve a given task.
European regulation and safeguards for LLMs in production
The European AI Act imposes specific obligations on providers of general-purpose AI models (GPAI) since August 2026. Fines can reach significant amounts for non-compliance. Any company using a LLM in Europe must verify the compliance of its provider with these new rules.
The obligations cover the technical documentation of the model, transparency regarding training data, and respect for copyright. For models classified as systemic risk, security assessments and adversarial testing are required.
In practice, this means that the choice of a LLM is no longer based solely on performance or price. Data traceability, the ability to audit outputs, and the location of hosting become full-fledged selection criteria, especially for regulated sectors (finance, health, legal).
Response evaluation: testing a LLM before trusting it
Deploying a model without an evaluation framework is like driving without a dashboard. Evaluation frameworks (LLM evaluation) allow for measuring the reliability of a model on specific tasks before production deployment.
- Define a test set with questions whose correct answers are known, then measure the model’s correct response rate.
- Test robustness by rephrasing the same question in several ways: a reliable model gives consistent answers regardless of the wording.
- Evaluate hallucinations by asking for information on fictional subjects. A well-calibrated model signals its uncertainty instead of inventing.
- Compare several models on the same test set to inform the multi-LLM routing strategy.
This evaluation step, often overlooked by teams eager to deploy, avoids costly errors in production and allows for justifying the choice of a model to decision-makers.
The landscape of LLMs is evolving rapidly, with frequent updates and new models every quarter. A reproducible evaluation framework allows for automatic retesting with each new version and determines whether a model change brings real gains or just a marketing effect.
