prompt-engineering-patterns

To get consistent structured output from an LLM, apply structured output patterns that enforce strict formatting constraints. This Skill provides prompt templates to reliably extract specific information and generate valid JSON responses.

Few-shot learning in LLM prompts is implemented by providing carefully selected examples within the prompt to guide model behavior. This Skill offers patterns to construct few-shot templates that enhance output quality and response consistency.

Chain-of-thought prompting improves complex reasoning by instructing the LLM to break down problems into intermediate steps. This Skill covers advanced chain-of-thought techniques to enhance performance and controllability in production applications.

Reduce token usage and optimize LLM output quality by applying prompt optimization techniques and efficient template systems. This Skill provides strategies to minimize unnecessary tokens while maintaining reliable application performance.

Yes, these prompt engineering patterns are designed for production chatbots and AI applications. They address scenarios requiring complex reasoning, consistent output formatting, and efficient token usage to ensure reliability and controllability.

If LLM output formatting is unreliable, apply system prompt design and structured output patterns to enforce formatting constraints. This Skill provides template systems to enhance response consistency across varied user queries.