Retrieval Augmented Generation

RAG answers you can trace to a source: ingestion, permissions, hybrid search, reranking, evals and regression gates, drawn and built in Python.

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.

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. 682 pages, 25 chapters, one evolving enterprise project, every line of code runnable in .NET 10.

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.

Build reliable, production-ready search systems with embeddings and neural reranking. Learn how to turn semantic meaning into relevant results, refine retrieved content and deliver better context for AI-generated answers. With hands-on Python examples and practical architectures, this book takes you from core concepts to real-world RAG systems.

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.

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.

We'll start with nodes, relationships and Cypher, as it'll make everything else easier later on. Then, we design a schema, chunk documents, extract entities and add embeddings. Then, we'll mix vectors with structure, run graph algorithms, think about rules and ontologies, and connect AI agents through safe, traceable tools. We'll also keep the graph fresh, measured and recoverable, because if it's stale, users won't trust it.

For PMs, presales, and managers who work with AI but don’t write code: how RAG, agents, fine-tuning, compliance, and security actually fit together.

HNSW powers almost every vector database, and few of the engineers who rely on it have seen inside it. Vector Search Inside Out builds it from first principles in 92 step-by-step figures, then tunes it on 1.18 million real vectors. You'll know exactly what M, efConstruction and ef do, and how to set them for your own data.

Curious about agentic AI but not sure where to start? This beginner-friendly guide takes you from the basics to building your own AI agents, even if you have no coding experience. Learn how agents work, choose the right tools and turn your ideas into practical, reliable solutions for real-world problems.

EmbeddingGemma 2 offers a hands-on look at Google's embedding model and how to put it to work. From semantic search and RAG to code retrieval and multimodal applications, this guide covers the architecture, trade-offs and practical implementation details you need to build reliable systems and make the right choices for your stack.

Build the engineering foundations required to design and develop production-grade AI systems—from Python and FastAPI to databases, security, semantic search, RAG, LLM architecture, and AI application performance.Go beyond Python programming and start thinking like an AI Engineer.The AI Engineer — Volume I takes you from professional Python engineering to the foundations of modern AI application development.You will build the engineering knowledge required to create reliable AI systems—not just experimental notebooks or simple API calls.A project-based AI engineering book covering Python production engineering, FastAPI, PostgreSQL, authentication, testing, reliability, embeddings, semantic search, RAG, LLM architecture, AI pipelines, caching, and performance optimization.AI EngineeringAI EngineerArtificial IntelligenceGenerative AILLMLarge Language ModelsPythonFastAPIRAGRetrieval Augmented GenerationSemantic SearchEmbeddingsVector SearchLLM ApplicationsAI SystemsMachine Learning EngineeringBackend EngineeringProduction AIAI ArchitectureSoftware Engineering

Build one real AI system end to end, from raw job postings to a deployed, tested API, and learn production AI engineering on the way. 24 chapters, runnable code, and numbers you can reproduce.

Unlock the full potential of large language models with RAG. This book takes you from core concepts to production-ready systems, exploring everything from embeddings and search to evaluation and security. With clear explanations, runnable code and practical insights, you'll learn how to build reliable AI systems grounded in the knowledge that matters.