Review Prompt Engineering

Prompt engineering is the deliberate design, testing, and revision of instructions and context used to guide an AI system toward a useful result.

People improve a request by clarifying the task, supplying relevant context, setting boundaries, showing examples, and checking the response. It is an iterative practice rather than a search for one magic phrase.

Clear inputs reduce avoidable ambiguity, but prompting cannot repair missing data, unsuitable models, broken workflows, or absent human review.

A team tests several versions of a document-summary prompt, compares results against a checklist, and records which context and constraints improve reliability.

Prompt engineering is not a guarantee of correctness and is not the whole discipline of working effectively with AI.

Design the request, test the result, and repair the conversation.

* [[prompt|Prompt]] * [[context-window|Context Window]] * [[conversation-drift|Conversation Drift]] * [[human-review|Human Review]]

* [Google Cloud, *Introduction to Prompt Design*](https://cloud.google.com/vertex-ai/generative-ai/docs/learn/prompts/introduction-prompt-design) — vendor-primary guidance on designing, testing, and iterating instructions and context for generative models. * [NIST, *Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile* (NIST AI 600-1)](https://doi.org/10.6028/NIST.AI.600-1) — risk-management context for prompts, prompt injection, testing, and human oversight.

* Use the Learning Lab's conversation-engineering material with [[prompt|Prompt]] and [[human-review|Human Review]].