# Prompt Engineering

> Prompt engineering is the practice of designing the instructions, context and examples given to a language model so it produces reliable, useful output for a specific task. It covers how the task is described, which reference material and examples are included, what format the output should take, and how edge cases should be handled. In production, prompts are versioned and tested like code.

## Why it matters for your business

Two businesses using the same model can get very different results depending on how the task is framed. Clear instructions, relevant context and a few good examples often fix problems that teams first try to solve with a bigger model or fine-tuning. Prompt changes can also break things quietly, because a wording tweak that improves one case may hurt another. Serious deployments keep a set of test cases and re-run them before any prompt change goes live, rather than editing prompts directly in production.

## Example

A marketing agency generates monthly client reports with AI, and early drafts read as generic. The team rewrites the prompt to include each client's goals, last month's report, a definition of each metric and two example paragraphs written by a senior strategist. They test the new prompt against 20 past reports before switching over, and the drafts need lighter editing.

Related terms: https://www.lumeor.studio/glossary/system-prompt/, https://www.lumeor.studio/glossary/context-window/, https://www.lumeor.studio/glossary/evals-ai-evaluation/, https://www.lumeor.studio/glossary/fine-tuning/, https://www.lumeor.studio/glossary/structured-outputs/

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Source: https://www.lumeor.studio/glossary/prompt-engineering/