prompt-engineering-patterns

To enforce structured JSON output, design prompts with explicit output constraints, reusable templates, and role-based system instructions. This approach ensures predictable, JSON-like responses for safer automation and reliable production workflows.

The best way to fix inconsistent few-shot extraction is applying prompt optimization techniques and explicit validation steps. Building relevant few-shot examples with clear output constraints guides classification and extraction tasks to produce reliable, consistent outputs.

Chain of thought reasoning improves LLM reliability by applying explicit verification steps and self-consistency patterns. These reasoning patterns guide the model through complex tasks, improving accuracy and controllability for production AI workflows.

Prompt engineering with this Skill requires numpy as a dependency to support its advanced scripts and components. It provides the computational environment needed to design, validate, and optimize system prompts and reasoning patterns.

LLM assistants drift off-format when prompts lack explicit validation steps and structured output templates. Designing robust prompt templates with role-based system instructions and clear output constraints enforces predictable, controllable behavior in production workflows.

Avoid few-shot examples when tasks require minimal context or when examples introduce bias that degrades output quality. Instead, apply prompt validation and verification techniques to determine if zero-shot reasoning patterns achieve better consistency.