RAG in Practice: Building AI Apps That Answer From Real Data (Practical AI Agent Design Book 2)
English | May 13, 2026 | ASIN: B0GZFPPH3T | 308 pages | Epub | 1.92 MB
Are you struggling with LLM hallucinations and unreliable answers in your AI applications? Retrieval-augmented generation (RAG) is the solution, and 'RAG in Practice' shows you how to build production-ready systems that answer from real data. Written by Nolan Hart, this practical guide takes you from raw documents to deployed applications, covering every stage of the RAG pipeline with minimal code and maximum insight.* Document ingestion: Turn PDFs, web pages, and messy files into structured, usable data with robust parsing and cleaning pipelines.* Chunking strategies: Learn fixed, semantic, and recursive chunking to split documents without losing meaning, preserving context across boundaries.* Embeddings and vector search: Understand how to map text to vectors, choose the right embedding model, and select vector databases like FAISS, Chroma, Pinecone, or Weaviate.* Hybrid search and reranking: Combine keyword matching, vector similarity, and metadata filters, then refine results with cross-encoders for precise evidence selection.* Prompt design for RAG: Structure prompts to inject retrieved context effectively, handle missing or conflicting information, and manage token budgets.* Citations and grounding: Build trust with source tracking, citation strategies, and fallback logic that reduces hallucination.* Evaluation metrics: Go beyond "looks good" with objective measures for retrieval quality, answer quality, and user trust, using a reusable evaluation harness.* Production hardening: Scale your system with caching, cost control, security, and role-based access, plus a debugging playbook for common failures.This book is for intermediate Python developers and AI engineers who have experimented with LLM APIs and want to move from demos to reliable, data-grounded applications. You'll gain a systematic roadmap for ingesting, chunking, embedding, retrieving, generating, and evaluating answers-all with a focus on architectural patterns that outlast tool churn.Whether you're building a document chatbot, a knowledge assistant, or an AI search tool, 'RAG in Practice' equips you to deliver answers your users can trust.
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