llm-app-patterns

Standard LLM application patterns include modular RAG pipelines for retrieval, ReAct-style agents for multi-step task execution, and LLMOps monitoring for observability. Using these established architectures ensures consistent tooling integration and reliable generation outputs.

Design a RAG pipeline by applying modular patterns for document ingestion, retrieval, and generation. Structuring the retrieval architecture with dedicated templates separates data fetching from generation, improving the accuracy of synthesized responses.

The best way to structure multi-step tasks is using plan-execute architectures with ReAct-style agents and function calling. This modular pattern allows the LLM to dynamically select tools, execute actions, and reason through intermediate steps autonomously.

Yes, you can use prompt IDEs to manage prompt templates, versioning, and chaining. This approach enables structured prompt experiments, allowing developers to iterate on modular prompts and compare generation outputs systematically.

LLMOps observability patterns improve reliability by applying evaluation and monitoring architectures to AI agents and RAG pipelines. These patterns provide visibility into tool integration and generation behaviors, allowing developers to detect anomalies and evaluate performance systematically.