Retrieval Augmented Generation

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.

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.

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.

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.  

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,

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.

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

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.

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.

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

Whether you are new to AI or building advanced applications, The Prompting Playbook gives you the knowledge and practical techniques to communicate effectively with large language models. Packed with research-backed insights, real-world examples and actionable guidance, it helps you get better results from AI with confidence.

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.

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.

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.