llm-app-patterns

Production-ready LLM app patterns are modular architectures for chat interfaces, tool-use agents, and guardrails. They enable scalable applications by standardizing RAG pipelines, prompt management, and safety practices like rate limiting without reinventing core orchestration.

To build a RAG pipeline for a customer support chatbot, you apply modular patterns that fetch relevant documents, use retrieval-augmented generation to formulate answers, and cite sources. This requires orchestrating data handling and LLM interactions efficiently.

The best way to implement guardrails and rate limiting in LLM applications is by applying modular safety patterns. These production-ready patterns integrate caching, observability, and robust safety practices directly into the chat interface and tool-use orchestration.

Yes, you need understanding of LLM orchestration, data handling, and robust safety practices to use ReAct-style agent architectures. These production-grade patterns require configuring tool use, managing streaming outputs, and implementing structured extraction.

Yes, you can use these LLM patterns for structured extraction and streaming outputs. The skill provides modular architectures specifically designed for building chat interfaces and tool-use agents that handle streaming data and extract structured information.

You should not use modular patterns for LLM applications if your project requires highly customized orchestration outside standard chatbot or assistant deployments. These patterns assume standard RAG pipelines and agent architectures, limiting highly specialized data handling.