prompt-engineering-patterns

Few-shot learning improves LLM response consistency by providing specific examples directly in the prompt. This Skill offers practical templates to implement few-shot examples, guiding the model to replicate the desired format and behavior for production applications.

Structured JSON output is best achieved through precise prompt engineering techniques. This Skill provides patterns and examples to constrain LLM responses into reliable JSON schemas, ensuring easier parsing and integration for production applications.

Chain-of-thought prompting enhances LLM reliability by guiding the model through intermediate reasoning steps before generating a final answer. This Skill covers implementing this technique to improve performance and controllability for complex tasks.

LLM prompts return inconsistent results due to ambiguous instructions or missing context. This Skill helps debug and optimize prompts by applying template systems, few-shot examples, and structured output generation to ensure reliable responses.

You can reduce token usage by applying prompt optimization techniques to streamline instructions. This Skill provides strategies to minimize token consumption while maintaining performance, reliability, and controllability in production LLM environments.

Designing system prompts for production LLM applications requires structured templates and optimization strategies. This Skill delivers comprehensive guidance on system prompt design, covering edge case handling, consistency improvements, and controllability.