本课程系统讲解企业级RAG生产落地，覆盖文档切片、混合检索、智能重排与引文落地，重点解决权限控制、提示词注入、数据合规及成本精算等深水区难题，助你交付可审计、可测、可控的生产级RAG系统。
原始标题：RAG Architecture: Production-Grade Retrieval Systems
本课程是一门聚焦于企业级生产环境落地的 RAG（检索增强生成）全栈架构进阶指南 。它围绕大模型在处理私有Fresh知识时无法回避的幻觉、数据过期、权限缺失等致命缺陷展开，通过 9 小时的高密度 Python 实战，深度传授包含文档切片、混合检索、智能重排及引文落地在内的全链路 RAG 管线设计。
课程最大的差异化卖点在于直面大多数网络教程选择性忽略的“企业级深水区”——即如何让安全访问控制（ACL）在向量检索中不失效，如何防范提示词注入与私密数据外泄，以及如何构建可审计、可彻底删除的合规架构 [2]。同时，它还引入了延迟预算、容量规划、红队攻防以及基于“单次成功回答”的成本精算模型，帮助技术人员彻底告别Demo阶段。
该课程非常适合正欲在私有企业知识库上构建 AI 特性的软件工程师、AI/ML 架构师、平台工程专家以及技术团队 Leader，通过对课程所附带的三套完整生产级方案及 Production-Readiness 标准的演练，学员将具备交付一套有据可查、权限合规、质量可测且成本可控的生产级 RAG 系统的核心软硬实力。
Last updated 10/2026
Created by Naveen Kumar Josyula
MP4 | Video: h264, 1920×1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: All Levels | Genre: eLearning | Language: English | Duration: 53 Lectures ( 9h 0m ) | Size: 2.9 GB
Build retrieval systems that answer from enterprise knowledge — cited, permissioned, measured, and cost-controlled.
What you’ll learn
⚡ Explain why raw language models fail on private, fresh, and permissioned enterprise knowledge
⚡ Design the full RAG pipeline: ingestion, chunking, embeddings, indexing, hybrid retrieval, reranking, grounded generation, and evaluation
⚡ Choose chunking, embedding, vector database, and indexing strategies using evidence instead of demos
⚡ Enforce access control, retention, deletion, and auditability in retrieval systems
⚡ Measure faithfulness, relevance, recall, latency, and cost per successful answer
⚡ Produce a production readiness plan for a real enterprise RAG system
Requirements
❗ Comfort reading Python and basic API/code examples
❗ General familiarity with LLMs, prompts, and embeddings is helpful but not required
❗ No prior vector database experience required
Description
This course contains the use of artificial intelligence.
Large language models fail on enterprise knowledge: they hallucinate, go stale at their training cutoff, and ignore access control. This course teaches you to build production-grade retrieval-augmented generation (RAG) systems that answer from your organization’s documents — with citations, permissions, measurable quality, and predictable cost.
Across 13 sections and 53 lessons (9 hours of video), you will design the complete RAG pipeline: document ingestion and preprocessing, chunking strategies, embeddings and vector representations, vector databases and indexing, hybrid search across dense, sparse, and reranking, prompt engineering for grounded generation, and rigorous evaluation of faithfulness, relevance, and recall.
Then you take it to production: latency budgets, cost models, scaling patterns, capacity planning, and load testing — plus the parts most courses skip: security, governance, and compliance. You will implement access control that survives retrieval, defend against prompt injection and data exfiltration, handle retention, deletion, and auditability, and run a red-team testing playbook.
Everything is demonstrated in working Python code shown on screen, including three full live-demo lessons, and the capstone has you design a complete production RAG system with a production-readiness review. You will learn to choose chunking, embedding, vector-database, and indexing strategies from evidence rather than hype, and to measure what matters: faithfulness, relevance, recall, latency, and cost per successful answer.
Built for software engineers, AI/ML architects, platform engineers, and technology leaders shipping AI features over enterprise knowledge. Follows the book RAG Architecture: Designing Production-Grade Retrieval Systems.
Who this course is for
⭐ Software engineers building AI features over enterprise documents and systems
⭐ AI/ML architects designing retrieval, search, and agent knowledge layers
⭐ Platform engineers responsible for vector search, indexing, and AI infrastructure
⭐ Technology leaders who need a defensible production plan for RAG
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