prompt-engineering-patterns

LLM prompt engineering uses few-shot learning and chain-of-thought techniques to maximize model performance and controllability. You can implement these patterns using provided production-ready templates and code examples to guide the model's reasoning process.

Getting structured outputs from an LLM requires designing system prompts and templates that enforce strict formatting rules. This skill provides patterns to reliably extract information and format it as JSON, handling errors gracefully to ensure production reliability.

Optimizing token usage involves designing efficient system prompts and reusable template systems. This skill facilitates dynamic example selection and template optimization to minimize token consumption while maintaining LLM performance and output reliability.

Prompt engineering patterns are highly effective for building reliable AI assistants for data extraction. By leveraging structured outputs, few-shot learning, and error handling, you can maximize LLM controllability and ensure accurate information extraction from user queries.

LLM outputs fail to follow structured formats due to unoptimized system prompts and lack of error handling. Implementing advanced prompt engineering patterns, such as dynamic example selection and structured output templates, resolves these reliability and controllability issues.