Standard RAG breaks in predictable ways. At enterprise scale, those failures compound- missed answers, rising costs, and compliance risks. This handbook gives you the architecture patterns to close the gap and build retrieval systems your organization can rely on.
What's inside:
Go beyond basic vector search - Catch the answers vector search misses by layering keyword matching, knowledge graphs, and smart query routing into one pipeline.
Fix retrieval at the source - What you retrieve depends on how you chunk. Learn chunking, parent-child structures that keep meaning intact as your knowledge base scales.
Handle queries static pipelines can't - Build systems that don't just retrieve, they evaluate, self-correct, and connect multiple sources to answer complex questions.
Debug RAG like you debug code - Instrument your pipelines with tracing, drift detection, and roundedness metrics. Know exactly where and why retrieval broke.
For AI engineers and PMs building systems where retrieval failures have business consequences.
