Leanpub Book LAUNCH 🚀 Retrieval-Augmented Generation: An Engineer's Guide to Building RAG Systems with Your Own Data by Jeroen Herczeg
Most teams trying to ship a RAG system stall at the prototype stage. The notebook works, the demo wins the meeting, the system never reaches users at scale.
Welcome to the Leanpub Launch video for Retrieval-Augmented Generation: An Engineer's Guide to Building RAG Systems with Your Own Data by Jeroen Herczeg!
Jeroen Herczeg
About the Book
Most teams trying to ship a RAG system stall at the prototype stage. The notebook works, the demo wins the meeting, the system never reaches users at scale. The gap between "this works on my laptop" and "this runs reliably in production" is wide and full of engineering challenges. This book is about that gap.
It's written for engineers who need to ship something real. Not for researchers writing benchmarks, not for managers picking vendors. For the person at the keyboard who needs to make decisions about chunking strategy, vector store choice, evaluation methodology, and production operations, and who's tired of vendor-shaped blog posts and examples that don't survive a deploy.
Each chapter pairs concept with implementation. Real code on a real corpus, runnable end to end. The seven failure points of a RAG pipeline are introduced in chapter 1 and traced through every subsequent chapter, so you learn to recognize where things break, not just patch them when they do.
The book
Why standalone LLMs fail on private data, what RAG actually is, and the building blocks underneath: embeddings, chunking strategies, vector storage (FAISS vs pgvector vs Qdrant with measured benchmarks), and a complete ingestion pipeline that handles the messiness of real documents.
Wiring retrieval into generation. Sparse vs dense retrieval, BM25, hybrid search with reciprocal rank fusion, reranking with cross-encoders, query transformation patterns (multi-query, sub-question decomposition, HyDE). Every chapter measures the improvement instead of just describing it.
Evaluation done right (separate retrieval and generation metrics, RAGAS, ablation testing). Hardening the pipeline (observability, semantic caching, citation systems, embedding staleness, cost optimization, load testing). Advanced retrieval patterns (GraphRAG, Corrective RAG, Self-RAG) with honest takes on when each earns its keep. Then agentic RAG with realistic guardrails for production.
By the end you'll be able to
- Choose a chunking strategy on retrieval evidence, not intuition
- Pick FAISS, pgvector, or Qdrant based on your actual constraints
- Build a RAG pipeline that handles real PDFs with OCR artifacts, encoding issues, and dirty markdown
- Evaluate retrieval quality separately from generation quality, and prove your changes help
- Add reranking, hybrid search, and query transformation when (and only when) they earn it
- Catch the seven failure points before they reach production
- Scale, monitor, and cost-optimize a RAG system that survives a deploy
About the Author
Jeroen Herczeg (@jeroenherczeg)
Jeroen Herczeg is a senior software engineer who builds AI systems for production.
He has 20 years of engineering experience across software platforms, distributed systems, microservices, Kubernetes, and product teams. His current work focuses on retrieval-augmented generation, AI agent orchestration, and practical AI engineering.
Most recently, he built the orchestrator agent for the Google + BBC AI Agents demo at IBC2025, winner of the Broadcast Tech Innovation Award. His interest in AI goes back to 2017, when he completed Udacity’s Artificial Intelligence Nanodegree. Today, that work has evolved into a focus on production RAG systems and AI agent orchestration.
He writes about practical AI engineering at herczeg.be/blog and lives in Belgium.
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