Interaction and usage
How to dialogue with an LLM, structure a prompt, manage a conversation. The everyday usage skills that make the difference between an experiment and a successful deployment.
- Context windowThe context window is the maximum amount of text an AI model can process in a single query, measured in tokens. It includes the prompt sent and the response to produce. Beyond this limit, the model loses access to the oldest information of the exchange.
- Conversation rolesConversation roles (system, user, assistant) are the labels assigned to each message in an exchange with an LLM. The system role defines the model's general behaviour, the user role carries the requests, the assistant role the model's past responses in the conversational history.
- HallucinationA hallucination is a factually false statement but formulated with confidence by a generative AI model. It stems from the statistical mechanism of generation: the model predicts the most likely next word, with no native capacity to verify. It is the main operational and legal risk of an enterprise AI deployment.
- Map and territoryThe map is not the territory. What you give an AI — prompt, files, context — is only a representation of the work. The real work happens elsewhere: in the code, the file, the constraints of the ground. The gap between the two is your unknowns. That is where, and almost only where, a mission derails.
- PromptA prompt is the natural-language instruction sent to an AI model to obtain a response. Its quality directly conditions the quality, relevance, and cost of the generated output. A good prompt is not long; it is precise, structured, and contextualised.
- Prompt engineeringPrompt engineering is the discipline of writing prompts methodically. It is not a rare profession but a transverse competence, which consists of formalising, testing, and standardising the instructions sent to an LLM to obtain reliable and reproducible outputs at the scale of an organisation.
- Prompting patternsPrompting patterns are formulation conventions that structure a prompt to orient the model's output. They do not exist as native LLM commands, only as guides for the writer. The distinction matters: no pattern triggers a feature, the effect comes from linguistic precision.