prompt-engineering-patterns

Improve prompt reliability by applying patterns like few-shot selection, chain-of-thought, and structured outputs to design, test, and iterate prompts with measurable, controllable outcomes. This enforces safe, bounded interactions for production-grade tasks.

Structure LLM reasoning using chain-of-thought and tree-of-thought patterns. These prompt engineering techniques break down complex problems into sequential steps, improving accuracy across coding, analytics, and documentation tasks.

Enforce structured outputs by defining a validation schema within your prompt. This enables reliable parsing, systematic error recovery, and consistent formatting for downstream data processing and analytics tasks.

Yes, you can use dynamic few-shot example selection for accurate task framing. This pattern adapts prompts across classification, code generation, and data tasks, ensuring repeatable outcomes tailored to specific domains.

Numpy is a required dependency for this skill. It supports the systematic evaluation and measurable improvements of versioned prompts, allowing you to track quality over time and maintain error recovery protocols.

Use role-based system prompts when you need progressive disclosure to tailor behavior for different domains and roles. This pattern bounds interactions safely and adapts framing for specialized coding or analytics tasks.