llm-app-patterns

Production-ready LLM applications require established patterns for RAG pipelines, agent architectures like ReAct, and LLMOps monitoring to ensure reliable performance. This Skill provides these architectural blueprints for designing and implementing AI agents.

Implementing a RAG pipeline involves applying specific LLM application patterns for retrieval and generation. This Skill offers comprehensive pattern libraries and architectural guidance to help you design and integrate RAG capabilities into your applications.

Agent architectures for LLM applications include ReAct, Function Calling, and Plan-and-Execute patterns. This Skill provides guidance on these architectures to help you select and implement the right agent design for your AI use case.

LLMOps observability and monitoring require specific metrics, logging, and evaluation frameworks. This Skill provides the necessary LLMOps patterns to track and improve your LLM application's performance throughout its lifecycle.

Yes, you can use this Skill for prompt IDE design as it includes templates, versioning, and chaining techniques. These prompt IDE patterns facilitate effective prompt design and management within your LLM applications.