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#prompting#ai-glossary

What is prompt engineering?

Prompt engineering is the practice of designing and refining the text you give to a language model so it produces more useful, reliable, and specific outputs.

Why it matters

Large language models are sensitive to how you ask. A vague request can produce a vague answer; a well-structured prompt can improve format, scope, tone, and task performance.

You reach for prompt engineering when you want to:

In practice, most teams use prompt engineering first because it is the fastest way to shape model behavior without retraining.

How it works

At a high level, a prompt is the input context the model uses to predict the next token. Prompt engineering is the craft of choosing that context well.

Common techniques include:

The idea is not magic control. It is more like better interface design for a probabilistic system: you reduce ambiguity, make the task easier to infer, and nudge the model toward the desired behavior.

For harder tasks, teams often combine prompt engineering with tools, retrieval, function calling, or evaluation loops. Prompting is usually the starting point, not the whole solution.

Tiny concrete example

Vague prompt:

Summarize this contract.

Better prompt:

Summarize this contract in 5 bullets for a non-lawyer.
Include:
- key obligations
- renewal terms
- termination conditions
- any unusual risks
Use plain English.

The second prompt gives the model a clearer task, audience, and output shape.

Common pitfalls / when NOT to use it

If the task needs strict correctness, strong security, or complex state, prompt engineering alone is usually not the right final solution.

Related terms

Related terms

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