Prompt Engineering
Prompt engineering is the practice of designing inputs that reliably produce desired outputs from large language models.
It covers foundational techniques, zero-shot, few-shot, chain-of-thought, and role prompting, as well as system prompt structure and structured output patterns for production AI systems. Getting prompts right often determines whether an AI application behaves consistently or unpredictably. Also known as: Prompting
What this topic covers
- Foundations — Prompt engineering is more systematic than it first appears: small phrasing choices trigger different reasoning paths inside a model, and understanding why reveals how LLMs process instruction.
- Implementation — Covers hands-on patterns for structuring system prompts, versioning templates, and applying few-shot examples, needed to ship prompt-driven features that behave consistently across edge cases in production.
- What's changing — Prompt engineering is evolving from manual craft toward automated evaluation and optimization.
- Risks & limits — How prompts are designed determines what models will and won't say.
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