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What is a prompt? Definition and business implications

A 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.

The prompt is the cornerstone of interaction with an LLM. Concretely, it is the text you type (or that your application sends in the background) to query the model. A minimal prompt can fit in one sentence; an elaborate prompt can include a role assigned to the model, a business context, examples (few-shot), a precise instruction, an expected output format, and negative constraints (“do not include X”). The quality of a prompt is measured against three criteria. Precision: no ambiguity about the expected output. Contextualisation: the model has the business elements it needs to answer. Structure: clean sections, clear hierarchy, instructions without contradiction. A bad prompt produces generic, off-topic responses, or confident hallucinations. A good prompt produces specific, actionable, reproducible responses. The same question, badly framed then well framed, can yield two radically different answers, with no change of model.

Concrete example

A salesperson at an industrial SME writes responses to calls for tender. Prompt 1 (minimalist): “Write me a response to this tender: [text]”. Output: generic, flat, no personalisation. Prompt 2 (structured): “You are sales lead at a French SME manufacturing machine tools, 80 staff, based in the Auvergne-Rhône-Alpes region. Reply to the tender below following this plan: (1) understanding the need, (2) three technical differentiators, (3) after-sales support, (4) delivery schedule. Cite our regional assets. Professional tone, no superlatives”. The output is usable on 80% of the time saved compared with manual drafting.

See also

Further reading

Anthropic prompt engineering documentation (external resource)

Sources

  1. Prompt engineering overview, Anthropic documentation 2026. https://docs.anthropic.com/en/docs/build-with-claude/prompt-engineering/overview (accessed 2026-05-24)

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