Open Daily: 10am - 10pm | Alley door pickup 10am - 7pm 3038 Hennepin Ave Minneapolis, MN
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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Retrieval-Augmented Generation (RAG) in Production: Build, Evaluate, Optimize, and Deploy Enterprise RAG Systems with Python

Retrieval-Augmented Generation (RAG) in Production: Build, Evaluate, Optimize, and Deploy Enterprise RAG Systems with Python

Mondal, Masud

Paperback

Series: AI Engineering, Book 6

General ComputersProgramming

ISBN13: 9798176915884
Publisher: Independently Published
Published: Sep 25 2026
Pages: 222
Weight: 0.67
Height: 0.47 Width: 6.00 Depth: 9.00
Language: English
Build production-grade Retrieval-Augmented Generation systems with Python - from raw documents to reliable, evaluated, and deployable RAG applications.

RAG is more than embedding documents and asking an LLM a question. A production system must retrieve the right evidence, enforce access controls, resist leakage, produce grounded answers, measure quality, handle changing corpora, and remain observable and recoverable in production.

Retrieval-Augmented Generation in Production takes you through that complete engineering journey with a practical Python implementation and a deterministic companion project.

You will learn how to:

  • Design index-time and query-time RAG pipelines with clear component contracts
  • Load and extract content from PDFs, DOCX, HTML, and Markdown
  • Normalize documents, preserve structure, and attach useful metadata
  • Design chunking strategies and understand their impact on retrieval quality
  • Build embeddings and vector indexes with FAISS and Qdrant
  • Combine dense and lexical retrieval using BM25 and reciprocal rank fusion
  • Apply metadata filtering and retrieval-time access controls
  • Improve precision with cross-encoder reranking
  • Rewrite and expand queries when the original question is difficult to retrieve
  • Assemble token-budgeted context while controlling duplication and ordering
  • Generate grounded answers with citations and explicit refusal behavior
  • Build golden sets and evaluate retrieval, groundedness, leakage, latency, and cost
  • Design deterministic evaluation harnesses that make results reproducible
  • Trace and operate RAG systems with production-oriented observability
  • Handle corpus updates, index generations, aliases, snapshots, rollback, and recovery
  • Build and deploy a production RAG API with FastAPI

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