llm-app-patterns

Architect a production-grade RAG pipeline using standardized patterns for advanced chunking, hybrid search, and contextual compression to ensure accurate retrieval and generation. These blueprints provide the structural foundation for robust document search applications.

The best architectural patterns for multi-agent workflows involve sophisticated orchestration using ReAct, function calling, and multi-agent collaboration. These agentic patterns enable AI systems to autonomously plan, execute tools, and coordinate complex tasks.

LLMOps evaluation ensures production reliability by establishing observability, prompt versioning, and automated evaluation frameworks. This monitors LLM application quality, tracks performance drift, and validates outputs against expected baselines.

You can scale LLM-based software systems with cost-efficient model usage by applying architectural blueprints designed for high-performance AI development. These patterns optimize resource allocation and model routing across the full application lifecycle.

You need contextual compression in your retrieval-augmented generation pipeline when retrieved documents contain excessive noise or irrelevant information. This technique filters and condenses context, improving generation accuracy and reducing token costs.