ai-patterns

LLM agent architectures like ReAct, Plan-and-Execute, and Multi-Agent Systems are implemented by integrating reasoning loops with external tool use. This provides structured patterns for agents to plan, act, and observe complex environments effectively.

Output structuring ensures LLM outputs match desired formats like JSON by applying specific prompt engineering techniques. You can enforce schema compliance using structured output patterns alongside libraries like LangChain and Instructor.

Context management strategies handle long conversations and large documents by optimizing token usage and retrieval. These patterns facilitate efficient context window processing within LLM frameworks to maintain coherent extended interactions.

Tool use and function calling patterns allow seamless integration of external APIs with OpenAI models. This capability enables LLMs to execute functions, fetch real-time data, and interact with external systems dynamically.

Security patterns protect against prompt injection and other LLM attacks by implementing input validation and output filtering. These techniques facilitate safe AI responses and guard against malicious exploitation in production environments.

Evaluating AI responses and optimizing LLM API costs involves using specific evaluation patterns to judge output quality. Cost optimization techniques manage expenses by strategically selecting models and reducing unnecessary token consumption.