prompting-pattern-library

Prompting patterns provide field-tested techniques like chain-of-thought reasoning and structured output formatting to systematically improve model performance. Apply these patterns to diagnose why a prompt fails and refine it across Claude, GPT-4, Gemini, and other LLMs.

Chain-of-thought prompting breaks complex reasoning into explicit steps, improving accuracy for multi-step problems. This pattern library documents when to use it versus structured outputs, direct answers, or role-based prompting depending on your task complexity.

Yes. This library provides model-specific guidance for Claude, GPT-4, Gemini, and related LLMs, showing which patterns work best on each and how to adapt techniques when switching models or building cross-model agent workflows.

Failure-diagnosis patterns help identify root causes—unclear instructions, ambiguous structure, or model limitations. The library catalogs common failure modes and provides targeted fixes for each, accelerating debugging across different use cases.

The library covers foundational to advanced patterns, so you can start with basics regardless of background. Patterns are organized by complexity and use case, making them accessible for learning and reference at any skill level.

Structured output patterns define schemas, enforce formatting rules, and validate model compliance. This library provides templates and model-specific techniques for generating JSON, XML, or other formats reliably across different LLMs.