Skip to main content

Announcing: Scaling Search and Retrieval for Contextual AI

· 3 min read
Nick Knize
Nick Knize
CEO & Founder, Lucenia | Creator of AWS OpenSearch

AI models are only as good as the context they can retrieve. Without the right data at the right moment, even the most powerful models fail. You might even say that search and retrieval is the most important layer of the AI stack.

I'm excited to announce that Scaling Search and Retrieval for Contextual AI is now in early release with O'Reilly Media.

Why This Book?​

As enterprises race to adopt AI, they're discovering that models are only as good as the context they can retrieve. Without fast, reliable, and secure access to the right data, AI becomes hallucination-prone and disconnected from reality. That's why retrieval has become the most important layer of the AI stack—and why engineers must understand how to design, scale, and operate it with precision.

Search infrastructure underpins nearly every modern AI and data application. OpenSearch and Elasticsearch alone are used by over 200,000 organizations globally. With the rise of LLMs, retrieval-augmented generation (RAG) has become a foundational design pattern—prompting companies like OpenAI, Meta, Cohere, and Google to emphasize retrieval-first architectures.

According to a 2024 McKinsey report, over 60% of enterprises deploying LLMs now integrate vector-based or hybrid search. This trend is accelerating, with hybrid and contextual retrieval being named a top 3 AI infrastructure priority by Andreessen Horowitz and a16z InfraRed.

What Makes This Book Different?​

This book takes a systems-first, vendor-neutral approach. Rather than explaining how to operate existing tools, it teaches you how to build the tools themselves. Whether you're modernizing an aging cluster, integrating RAG into your LLM pipeline, or simply trying to understand what makes search and retrieval tick, this is your blueprint.

The book explores the full lifecycle of search systems—from indexing and query execution to sharding, vector search, hybrid retrieval, and real-world AI integration.

What You'll Learn​

By the end of this book, you will:

  • Architect search and retrieval systems that enable scalable, performant, and secure AI inference
  • Navigate the trade-offs between indexing and retrieval models
  • Apply proven patterns to build fault-tolerant, efficient search infrastructure
  • Support hybrid and AI-native workloads with structured, unstructured, and vector data
  • Optimize performance, storage, and resilience across varied deployment topologies and constraints

Who Is This For?​

This book is written for backend engineers, infrastructure architects, and AI/ML practitioners who are building or integrating search and retrieval systems into their applications. Many readers will be responsible for scaling internal search platforms, powering AI pipelines (e.g., RAG), or evaluating what is needed for efficient search and information retrieval to support modern contextual AI in their organization.

I assume readers are proficient with backend systems (e.g., REST APIs, concurrency, indexing), familiar with distributed systems concepts (e.g., sharding, replication), and likely have read Designing Data-Intensive Applications, Relevant Search, or technical blog series from companies like Netflix, Uber, or Meta AI.

Hands-On with Lucenia​

All examples in this book use Lucenia, a free scalable search and retrieval system for contextual AI. As chapters are finalized, code examples will be posted in the Examples section of this site.

Get Early Access​

The book is currently in early release. If you're interested in becoming a technical reviewer and shaping the final content, sign up here.

Stay tuned for chapter previews and deep dives into the topics covered in the book.