prompting-patterns

What problem does it solve?

This Skill provides a structured approach to prompt engineering, addressing challenges like inconsistent LLM responses, prompt injection vulnerabilities, and context window limitations. It ensures your LLM applications are reliable, secure, and deliver high-quality outputs.

Core Features & Use Cases

• Structured Prompt Templates: Guides on creating reusable prompt templates with system messages, variables, and few-shot examples using Pydantic.
• Prompt Injection Prevention: Provides patterns for sanitizing user input and wrapping it with clear boundaries to mitigate injection risks.
• Few-Shot & Chain-of-Thought: Explains how to use few-shot examples for complex tasks and chain-of-thought prompting for detailed reasoning.
• Context Window Management: Offers strategies for truncating message history and managing context window limits for long conversations.
• Prompt Version Control: Introduces patterns for versioning prompts and tracking their performance metrics.
• Use Case: A developer is building an LLM-powered summarization tool. This skill helps them create a PromptTemplate with a clear system message, few-shot examples for better accuracy, and integrate PromptSanitizer to protect against malicious user inputs.

Quick Start

Create a structured prompt template for summarizing documents, including a system message and a few-shot example.