Advanced RAG Patterns
Standard RAG breaks at enterprise scale: missed answers, costs, compliance risks. Architecture patterns to build retrieval systems you can rely on.
For AI engineers and PMs building systems where retrieval failures have business consequences.
Read our in-depth eBook to:
- 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
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What's inside
5 chapters · ~80 pages
Beyond Vector Search
Layering keyword matching, knowledge graphs, and smart query routing.
Advanced Chunking Strategies
Parent-child structures that preserve meaning at scale.
Agentic Retrieval Pipelines
Systems that evaluate, self-correct, and connect multiple sources.
RAG Observability
Tracing, drift detection, and groundedness metrics for debugging.
Production Patterns
Architecture patterns for enterprise-scale reliability.
More in this series
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The Agentic RAG Playbook
Transform RAG theory into product-ready enterprise solutions that deliver measurable business impact across agentic workflows and retrieval systems.
Mastering AI Agent Evaluation
AI agents are easy to spin up and dangerously hard to trust in production. A concrete evaluation playbook to turn messy agents into controlled systems.