prompt-engineering-patterns

Few-shot learning improves LLM prompt accuracy by providing example inputs and outputs within the prompt to guide the model's responses. This Skill provides templates to systematically apply few-shot techniques for production applications.

Chain-of-thought prompting directs an LLM to break down its reasoning process step-by-step before delivering a final answer. You should use this technique to enhance reliability and controllability in complex reasoning scenarios.

Designing system prompts for production chatbots involves creating structured instructions that define behavior and output quality. This Skill offers advanced patterns to refine system prompts and minimize token usage while maintaining control.

The best way to reduce token usage and latency is through systematic prompt optimization workflows. This Skill provides techniques to refine instructions, apply prompt templating, and minimize unnecessary tokens without sacrificing output quality.

Yes, you can use prompt templates to standardize LLM outputs across various application development scenarios. This Skill provides templating patterns to ensure consistent performance, reliability, and controllability in production environments.