AI Engineering Bookshelf 2026: 6 LLM Engineering eBooks Worth Reading on Google Play Books
Building with large language models has moved on from clever prompts. In 2026 the real work is retrieval, tool access, agents, monitoring and security, and each of those is now a discipline of its own. ChatStick Team has published a run of short, practical engineering eBooks on exactly these topics, and this reading list, AI Engineering Bookshelf 2026, picks six that together cover the full life of an LLM application, from the first retrieval pipeline to the security review before launch. All six are English-language eBooks on Google Play Books, each with a free sample.
For every book you will find what it covers (taken from its official description), its length and who it suits best, followed by a suggested reading order.
1. Building Production RAG Systems
Most teams can get a Retrieval-Augmented Generation demo running in an afternoon; the hard part is latency, retrieval quality and runaway costs once real users arrive. This guide is written for exactly that step. It covers vector databases such as Pinecone, Weaviate and pgvector, fine-tuning embedding models, semantic chunking, hybrid search that combines dense and sparse retrieval, cross-encoder reranking and automated evaluation with RAGAS.
At a glance: 88 pages · published May 2026 · around US$2.99. Best for engineers whose RAG prototype works but is too slow, too expensive or not accurate enough.
2. The Model Context Protocol Developer's Handbook
The Model Context Protocol is fast becoming the common way to connect AI agents to tools, data and APIs, and this handbook is a developer-focused reference to it. It explains the MCP architecture, walks through building servers in Python and Node.js, and covers authentication and security, deployment patterns and integration with Claude, GPT, Gemini and local models. It is aimed at backend engineers, AI builders and enterprise architects.
At a glance: 85 pages · published May 2026 · around US$2.99. Best for backend developers who want to expose their own tools and data to AI assistants.
3. AI Agents in Production
This is the generalist's book in the set: a handbook for moving AI agents from prototype to secure, scalable production. It covers ReAct and Plan-and-Execute architectures, MCP integration, handling hallucinations and output variability, observability with tracing and metrics, prompt-injection defences, scaling with message queues and parallel processing, and continuous evaluation, with examples in Python and Node.js.
At a glance: 80 pages · published May 2026 · around US$2.99. Best for developers shipping their first agent and wanting one broad checklist.
4. Multi-Agent AI Systems
When one agent is not enough, this guide takes over. It is a hands-on engineering book on building, deploying and monitoring multi-agent systems with LangGraph 1.0, CrewAI, AutoGen 0.4 and OpenAI Swarm. Topics include stateful workflows, group-chat topologies, dynamic task delegation, failure-mode analysis, state management, human-in-the-loop design, cost optimisation through prompt caching and model tiering, and testing with distributed tracing and LLM-as-a-judge evaluations.
At a glance: 81 pages · published May 2026 · around US$2.99. Best for teams already choosing between LangGraph, CrewAI and AutoGen.
5. LLM Observability in Production
You cannot fix what you cannot see. This book builds observability for LLM systems from basic instrumentation up to automated evaluation: OpenTelemetry tracing, Langfuse for prompt versioning and A/B testing, RAGAS and DeepEval for measuring faithfulness and hallucinations, monitoring agentic workflows and Arize Phoenix for embedding drift. It also covers evaluation datasets, feedback loops and production concerns such as scalability, security and compliance, for ML engineers and senior architects.
At a glance: 90 pages · published May 2026 · around US$2.99. Best for ML and platform engineers responsible for quality once the system is live.
6. LLM Security Engineering
The security title closes the shelf. Written for security engineers, penetration testers and AI developers, it maps the LLM threat landscape through the OWASP GenAI Top-10 and Agentic AI Top-10, teaches structured red-team engagements with open-source tools such as promptfoo and Garak, and explains how to detect and block direct and indirect prompt injection. It also covers hardening multi-agent systems, model-extraction protection, supply-chain and dataset-poisoning risks, and building an enterprise AI security program with risk assessments, monitoring and incident-response playbooks.
At a glance: 80 pages · published May 2026 · around US$2.99. Best for security teams and anyone who has to sign off an LLM feature before launch.
Suggested reading order
Building your first LLM product? Start with Building Production RAG Systems, then AI Agents in Production — retrieval first, actions second.
Connecting AI to your own tools? Read The Model Context Protocol Developer's Handbook next to AI Agents in Production; both treat MCP as a core building block.
Scaling to many agents? Multi-Agent AI Systems is the deep dive on orchestration, state and cost once one agent is no longer enough.
Already in production? Go straight to LLM Observability in Production and LLM Security Engineering, and add our earlier pick LLMOps Engineering for the full lifecycle view.
Who this reading list is for
Software and backend engineers adding AI features to existing products; ML and platform engineers who own reliability and cost; solution and enterprise architects comparing approaches; security engineers who need a structured view of LLM risk; and technical leads who want their team to share the same vocabulary. Because each book is around 80–90 pages, the set also works well for self-study: one book per week gives a solid overview of production LLM engineering in a month and a half.
Our take
These are compact, practitioner-oriented guides rather than exhaustive textbooks or academic references. Their strength is focus: each one picks a single production problem and names the concrete tools and techniques used to solve it today — pgvector and hybrid search, MCP servers, LangGraph and CrewAI, OpenTelemetry and Langfuse, promptfoo and Garak. The trade-off is depth: at this length, readers who need line-by-line implementations will still want vendor documentation, and a field that changes this quickly will date some specifics. As a fast, affordable map of where LLM engineering stands in 2026, the bookshelf is a strong starting point. Open the free samples of two or three and begin with the problem you face right now.
Frequently asked questions
Where can I buy these AI engineering books?
All six are sold as eBooks on Google Play Books for around US$2.99 each (local prices vary by country). Each book page has a free sample, and you buy with your Google account.
Can I read them on an iPhone?
Yes. Install the free Google Play Books app on iPhone or iPad, or read in any web browser at play.google.com/books. Android phones, tablets and supported e-readers work too.
What language are they in, and how long are they?
All six are in English and were published in May 2026. They range from 80 pages (AI Agents in Production and LLM Security Engineering) to 90 pages (LLM Observability in Production), roughly one and a half to two hours of reading each.
Who is ChatStick Team?
ChatStick Team publishes accessible eBooks on AI and technology engineering, cars and EVs, film and music legends, world history, mythology, and science and nature. You can browse the full catalogue on Google Play Books.
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