Advanced RAG assembles embeddings, retrievers, LLMs, and user interfaces into an orchestrated pipeline. Separating retrieval from generation enables private, current knowledge without retraining, while increasing operational complexity. section 12.4.4
Production deployments must coordinate:
- Performance and reliability across distributed pipeline components. section 12.4.4
- Observability for retrieval quality, prompts, token consumption, costs, and multistep failures. section 12.4.4
- Responsive interfaces with streaming responses, source attribution, and graceful error handling. section 12.4.4
Operational RAG application
A production pipeline can combine a vector-backed retrieval stage, an LLM generation stage, and a Chainlit interface, then use Langfuse to inspect retrieval, prompts, tokens, and costs. section 12.4.4
