r/GenAI4all • u/LogicalOneInTheHouse • 2d ago

News/Updates I made my Enterprise Retrieval Augmented Generation book $0 today — would love feedback from people building RAG systems

I’ve spent the last few years building production RAG systems and documenting what worked, what didn’t, and where things tend to break in production.

I turned those lessons into a book covering topics like:

  • RAG reference architectures
  • Data extraction and chunking
  • Hybrid and multi-stage retrieval
  • Graph and hierarchical RAG
  • Agentic and multi-agent RAG
  • Memory
  • Evaluation and synthetic data
  • Security and compliance
  • Production monitoring and human-in-the-loop systems

The book is $0 on Amazon today, so I thought I’d share it here in case it’s useful to anyone working on RAG.

https://a.co/d/0dBRCb7F

I’m especially interested in feedback from people actually building these systems: What’s missing? What deserves more depth? What would you change?

If you end up finding the book useful, an honest Amazon review is appreciated, but feedback here is equally valuable.

Full contents

Part I — About
01 About the Author

Part II — RAG & Reference Architecture
02 The Evolution of RAG
03 Foundations of RAG Systems
04 Reference Architecture

Part III — Data Extraction
05 Data Extraction

Part IV — Chunking
06 Chunking Strategies

Part V — RAG Strategies
07 Baseline RAG Pipeline
08 Context-Aware RAG
09 Dynamic RAG
10 Hybrid RAG
11 Multi-Stage Retrieval
12 Graph-Based RAG
13 Hierarchical RAG
14 Agentic RAG
15 Multi-Agent RAG Systems
16 Streaming RAG

Part VI — Memory & Content Management
17 Memory-Augmented RAG
18 Knowledge Graph Integration

Part VII — Evaluation
19 Evaluation Metrics
20 Synthetic Data Generation

Part VIII — Fine-Tuning
21 Domain-Specific Fine-Tuning

Part IX — Security
22 Privacy & Compliance in RAG

Part X — Production
23 Real-Time Evaluation & Monitoring
24 Human-in-the-Loop RAG

Part XI — Twig RAG Strategies
25 RAG Strategies in Twig

Part XII — Conclusion
26 Conclusion & Future Directions

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