Your search system is failing on 30-40% of queries. You just can't see it.
Keyword search misses semantic matches. Vector search confuses entities and returns confidently wrong results. The answer is not choosing one over the other. It is combining them.
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Why this book exists
Keyword search has dominated information retrieval for thirty years, and it fails silently. Two people will choose the same term for the same concept less than 20% of the time (Furnas et al., 1987), which means users routinely phrase queries the index cannot match, and neither they nor the system notice.
Vector search closes that vocabulary gap by matching meaning instead of words, but it introduces a new class of failures. Exact-match precision degrades. Entities get confused. Similarity scores look confident on nonsense queries. Negation slips through unnoticed.
No existing resource treats hybrid search as a system design problem. Vendor documentation is biased toward the product it is selling. Academic papers are fragmented across a decade of venues and do not connect to production concerns. This book is written to fill that gap.
Who this book is for
Written for engineers and leaders responsible for shipping retrieval that actually works in production.
What makes this different
System design focus
Not a textbook. Not a tutorial. Architecture patterns and their consequences for teams making real decisions.
Vendor-neutral
No platform allegiance. Honest trade-offs for Elasticsearch, OpenSearch, Pinecone, Weaviate, Qdrant, Milvus, pgvector, and managed cloud services.
Production-tested
Every pattern addresses latency budgets, cost constraints, and operational complexity. Math is in the appendices. Migration plans are in the playbook.
What's in the book
Six parts, twenty chapters, three appendices. Each part stands on its own for readers who need to enter at a specific layer of the stack.
Part I
Why Hybrid Search
After Part I, you'll understand exactly where keyword and vector search fail and have a decision framework for when hybrid retrieval is worth the complexity.
- 1. The Limits of Keyword Search
- 2. The Limits of Vector Search
- 3. The Case for Hybrid
Part II
Architecture
After Part II, you'll be able to design a hybrid search system on paper and choose the platform to build it on.
- 4. Hybrid Search Architecture Patterns
- 5. Query Understanding
- 6. The Reranking Stage
- 7. Choosing Your Search Platform
Part III
Models
After Part III, you'll know which embedding and reranker models to select, when to fine-tune, and how to train domain-specific models.
- 8. Embedding Model Selection
- 9. Fine-Tuning Embeddings for Your Domain
- 10. Choosing and Training Reranker Models
Part IV
Evaluation
After Part IV, you'll have a complete methodology for measuring search quality, from offline metrics through production A/B testing.
- 11. Search Quality Metrics
- 12. Building an Evaluation Pipeline
- 13. Online Evaluation and Experimentation
Part V
Production Operations
After Part V, you'll know how to index at scale, meet latency budgets, monitor quality, and manage infrastructure cost.
- 14. Indexing at Scale
- 15. Latency, Throughput, and Scaling
- 16. Monitoring and Observability
- 17. Cost Optimization
Part VI
Applied Domains
After Part VI, you'll have domain-specific playbooks for the three largest hybrid search deployment categories.
- 18. Hybrid Search for RAG Pipelines
- 19. E-Commerce Product Search
- 20. Enterprise Knowledge Search
Read the introduction and first two chapters free
See how BM25 fails on 30-40% of queries and why vector search creates a different class of silent failures.
Get Sample
You will receive the introduction and the first two chapters in PDF.
About the author
Laszlo Csontos, author of Designing Hybrid Search Systems.
Laszlo Csontos builds and improves search systems: hybrid retrieval, custom embedding models, rerankers, and RAG pipelines. He writes about the engineering trade-offs that show up when lexical and vector retrieval have to live in the same pipeline under real latency and cost constraints.
This book is the resource he wishes had existed when he started working on production search.