Leanpub Header

Skip to main content

Filters

Category: "Retrieval Augmented Generation"

Books

  1. Production-Grade RAG with C# and .NET
    Production-Grade RAG with C# and .NET
    Building Retrieval-Augmented Generation Systems with C#, the Microsoft Agent Framework, and Azure
    Rachid DAHIR

    Build production-grade RAG systems in C# — from an 80-line Hello World to a fully deployed Azure pipeline with the Microsoft Agent Framework, MCP, GraphRAG, multi-agent orchestration, eval gates, and EU AI Act-ready audit trails. 668 pages, 25 chapters, one evolving enterprise project, every line of code runnable in .NET 10.

  2. Mastering Qdrant for RAG Applications
    Mastering Qdrant for RAG Applications
    Building Production Vector Search Systems with the Open-Source Vector Database
    Steve Publications

    Mastering Qdrant for RAG Applications is your practical guide to building production-ready RAG systems with the leading open-source vector database. Learn how to design, optimize, and scale high-performance vector search using Qdrant through clear explanations, real-world examples, and hands-on code.

  3. LLMs Explained
    LLMs Explained
    How Large Language Models Learn, Think, and Generate
    Steve Publications

    Large language models are changing the world, yet few people understand how they actually work. This book cuts through the hype, explaining the ideas behind modern AI with clarity, precision, and no unnecessary jargon.

  4. Local AI Search Engine
    Local AI Search Engine
    Building Intelligent Document Retrieval Systems from Scratch
    Steve Publications

    Learn how to build a fast, private AI search engine that indexes and retrieves documents using modern open-source tools. From a simple prototype to a production-ready system, you will create intelligent local search that runs entirely on your own hardware.

  5. TypeScript in the AI Era
    TypeScript in the AI Era
    Building Modern Applications with Type-Safe Code and Intelligent Systems
    Steve Publications

    Build production-ready applications with TypeScript in the age of AI. Go beyond the basics to master type-safe development, modern full-stack architectures, LLMs, intelligent agents, tool calling and the Model Context Protocol. With practical, runnable examples throughout, this book shows you how to build, test, secure and deploy serious TypeScript AI systems.

  6. Production LLMOps
    Production LLMOps
    Designing, Deploying, and Operating Large Language Model Systems at Scale
    Steve Publications

    Building an LLM demo is easy. Running one reliably at scale is not. Production LLMOps shows you how to design, deploy and operate real-world LLM systems, covering RAG, agents, fine-tuning, evaluation, CI/CD, observability, security and more, with practical code you can adapt for production.

  7. Production RAG and GenAI Systems

    Learn how to design and build production-ready RAG and GenAI systems—from retrieval fundamentals and evaluation to agents, observability, security, and end-to-end architecture.

  8. Python AI Programming, Second Edition
    Python AI Programming, Second Edition
    Kickstart developing AI-ready apps with RAG, DSPy, MCP, agents, evals, observability and open-source models
    GitforGits | Asian Publishing House

    Vectors, embeddings, retrieval, agents, and evaluation are all built from first principles inside the chapter that needs them. No mathematics. No machine learning background. No prior AI experience and no framework knowledge is required. We build with plain Python and small, single-purpose libraries.  

  9. Agent Development Kit
    Agent Development Kit
    on Google Cloud Platform
    Sudhanshu Jaiswal

    What if your AI didn’t just answer questions—but solved problems?Imagine an AI that doesn’t just chat—it plans, reasons, and takes action. An AI that can fetch data, run diagnostics, collaborate with other agents, and even handle your most tedious tasks—all while you focus on what matters.Google’s Agent Development Kit (ADK) makes this possible,

  10. Redis for AI Applications: Building Fast, Intelligent Systems
    Redis for AI Applications: Building Fast, Intelligent Systems
    A Practical Guide to Caching, Vector Search, RAG, LLM Integration, and Production Deployment with Python
    Steve Publications

    Build smarter AI applications with Redis at the core. This practical guide shows you how to use vector search, RAG, semantic caching, agent memory and real-time inference to create fast, scalable systems. With runnable Python examples throughout, you’ll learn how to take AI projects from prototype to production.

  11. Failure-First AI Agents
    Failure-First AI Agents
    A Practical Field Guide to Evaluating RAG, Tool Use, Memory, and Multi-Agent Systems
    storymaker

    A hands-on, failure-first guide to evaluating LLM agents, RAG, tool use, grounding, and production release gates—with executable Python examples and tests.

  12. Building AI Agents with Ollama
    Building AI Agents with Ollama
    Design, Deploy, and Scale Reliable Local AI Systems
    Steve Publications

    Build powerful AI agents that run entirely on your own hardware with Ollama. Learn practical patterns for RAG, multi-agent systems, security and deployment through clear explanations and production-ready code you can use right away. From first setup to reliable real-world applications, this book helps you build with confidence.

  13. AI for Financial Risk, Compliance and Regulatory Reporting
    AI for Financial Risk, Compliance and Regulatory Reporting
    Production Systems and Architecture for Modern Banking
    Sreedhara Reddy Kotha

    A £4.1M PRA compliance failure. A fictional bank called Avon & Wessex. Sixteen chapters and 23 systems showing exactly how to build production-grade, auditable AI for credit, market, operational and liquidity risk, model governance, and regulatory reporting — the practitioner's guide UK banking has been missing.

  14. Beyond "Chunk and Pray"
    Beyond "Chunk and Pray"
    Building Trustworthy RAG with Geometric Knowledge Graphs
    Agus Sudjianto and Wing Yan Lau

    Top-k is not relevance, retrieved text is not evidence and an LLM judging another LLM is not verification. Beyond “Chunk and Pray” shows how to build RAG that answers through a verified knowledge graph, preserves exact numbers, cites its sources and abstains when it cannot prove the answer.

  15. Agent Engineering with Gemini
    Agent Engineering with Gemini
    Book 1: Build an Inspectable Agent MVP with Gemini, ADK, and Antigravity
    Venkatesh Tadinada

    Move beyond agent demos and learn to engineer reliable AI systems with Gemini, ADK, and Antigravity. Build the WidgetWare SDR while mastering context, tools, skills, structured workflows, evidence-backed research, multi-agent coordination, human approval, evaluation, deployment, and controlled loops. Includes all future Early Access update