Definition
Prompt engineering is designing the instructions, context and examples given to a language model so it produces accurate, consistent output for a task.
Prompt engineering explained
The same model can produce excellent or useless output depending on how it is instructed. Prompt engineering covers the system instructions that set the model's role and rules, the context it receives, examples of good output, the required format, and how edge cases should be handled.
Practical techniques include:
- Stating the goal, audience and constraints plainly, including what not to do.
- Giving relevant context, such as documents or data, rather than relying on the model's memory.
- Showing one or two examples of the output you want.
- Asking for structured output, such as JSON, when another system will read it.
- Testing against a set of real inputs and refining based on failures.
For business tools, prompts are part of the product and deserve version control and testing like code. A small wording change can shift results across thousands of runs, so changes should be checked against a fixed set of test cases before going live.
Example
Your content team's AI drafts keep sounding generic. Rewriting the prompt to include your style guide, a target reader, two example paragraphs and a rule to cite a source for every figure makes the first drafts usable with light editing.
Why it matters
Well-engineered prompts are the difference between an AI tool that saves time and one that needs every output rewritten.
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Related terms
- LLM (Large Language Model)An LLM (large language model) is an AI model trained on huge amounts of text to generate language, such as the models behind ChatGPT, Claude and Gemini.
- AI agentAn AI agent is software that uses a language model to choose and take actions, such as searching, reading files or updating systems, to finish a task.
- Context windowA context window is the most text, measured in tokens, that a language model can consider at once, including instructions, documents and its reply.
- AI hallucinationAn AI hallucination is a confident but false statement from a language model, such as an invented fact, quote, citation or product detail.