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Glossary · AI

Prompt engineering

Prompt engineering is the practice of writing, structuring and testing the instructions, context and examples given to a language model so it produces useful, consistent output for a specific task.

In one line

Prompt engineering is the practice of writing, structuring and testing the instructions, context and examples given to a language model so it produces useful, consistent output for a specific task.

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Why prompt engineering matters

The same model can give very different results depending on how a task is framed. Clear prompts reduce errors, cut costs and make behaviour more predictable for users.

How prompt engineering works

A good prompt states the role, the task, the rules and the output format. It may include examples of good answers and the documents the model should use.

Prompts are then tested against a set of real inputs and improved based on where they fail.

Common prompt engineering mistakes

Tweaking a prompt until one demo looks good. Putting security rules only in the prompt. Not versioning prompts, so nobody knows what changed when quality drops.

How Gr8r approaches prompt engineering

We treat prompts like code: versioned, reviewed and tested against an evaluation set before release. Rules that matter, such as permissions, are enforced in the application, not just the prompt.

Example

A prompt for summarising sales calls specifies the five headings to use, asks the model to quote the customer for any objection, and says to write "Not discussed" rather than guess.

FAQ

Questions

  • Is prompt engineering still needed with newer models?

    Yes. Newer models follow instructions better, but clear task framing, examples and testing still make a large difference to reliability.

  • Should prompts be kept secret?

    Treat them as internal, but assume they can leak. Never rely on a hidden prompt to protect data or enforce permissions.

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