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Advanced RAG Engineering: Build Production-Ready Enterprise

Name: Advanced RAG Engineering: Build Production-Ready Enterprise
Availability: InStock
Rating: 1 (18 reviews)
Author: Arjun Vaid

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Build enterprise RAG with hybrid search, GraphRAG, evaluation, security, governance, and observability

 1/5

(18) Ratings

2 students

IT & Software

Created by Arjun Vaid

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What you'll learn

Design enterprise RAG architectures that balance retrieval quality, grounding, latency, cost, security, governance, and maintainability.
Build local ingestion pipelines for PDFs, HTML, Office files, spreadsheets, tables, and scans with metadata, versioning, deduplication, and lineage.
Apply fixed, recursive, structure-aware, semantic, hierarchical, and parent-child chunking to improve retrieval and context quality.
Implement hybrid retrieval with keyword search, dense embeddings, metadata filters, Reciprocal Rank Fusion, and CPU-based re-ranking.
Improve retrieval with query rewriting, multi-query generation, decomposition, HyDE, intent checks, and retrieval routing.
Build Self-RAG and Corrective RAG workflows with grading, correction, verification, retry limits, cost controls, and abstention.
Create CPU-friendly GraphRAG pipelines with entities, relationships, resolution, provenance, graph traversal, and multi-hop retrieval.
Build CPU-only retrieval for OCR, layout-aware parsing, tables, figures, page-level search, modality routing, and visual citations.
Evaluate RAG systems with golden datasets, retrieval metrics, grounding checks, citation tests, local judges, and regression suites.
Prepare RAG systems for production with tracing, caching, cost estimation, ACL-aware retrieval, tenant isolation, security, CI/CD, and deployment.

This course includes:

20.5 total hours on-demand video

0 articles

9 downloadable resources

83 lessons

Full lifetime access

Access on mobile and TV

Certificate of completion

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Course content

Requirements

Intermediate Python programming experience is recommended.
Basic familiarity with large language models, embeddings, vector search, and standard RAG workflows will be helpful.
Familiarity with REST APIs, JSON, Git, command-line tools, and basic Docker usage is recommended.
A computer capable of running Python and local Docker services is required; 16 GB of system memory is recommended.
No paid AI API, cloud account, managed database, or dedicated GPU is required for the mandatory course demonstrations and Hands on Labs.

Description

Post Views: 11

This course contains the use of artificial intelligence.

Move beyond basic Retrieval-Augmented Generation demos and learn how to design, build, evaluate, secure, and operate production-ready enterprise RAG systems.

In this hands-on course, you will build an Enterprise Knowledge Intelligence Platform that evolves throughout the curriculum. You will begin with a baseline RAG application and progressively add advanced ingestion, chunking, retrieval, query enhancement, adaptive workflows, graph-based retrieval, multimodal document processing, evaluation, observability, security, and deployment capabilities.

You will learn how to process PDFs, HTML, Office documents, spreadsheets, tables, and scanned files. You will compare fixed, recursive, structure-aware, semantic, hierarchical, and parent-child chunking strategies while preserving metadata, document versions, permissions, and source lineage.

You will implement hybrid retrieval using keyword search, dense embeddings, metadata filters, Reciprocal Rank Fusion, and CPU-compatible cross-encoder re-ranking. You will improve retrieval relevance with query rewriting, multi-query generation, query decomposition, Hypothetical Document Embeddings, intent-preservation checks, and retrieval routing.

The course also covers Self-RAG and Corrective RAG patterns. You will build bounded workflows that grade retrieved evidence, correct failed retrieval, verify generated answers, enforce retry limits, and return grounded no-answer responses when reliable evidence is unavailable.

You will create a CPU-friendly GraphRAG pipeline for entity and relationship modeling, entity resolution, provenance, graph traversal, multi-hop retrieval, and hybrid graph-document search. You will also build a CPU-only multimodal retrieval workflow for OCR, layout-aware parsing, structured tables, figures, page-level search, and visual citations.

To prepare the application for production, you will create golden evaluation datasets and measure Recall@K, Precision@K, MRR, nDCG, grounding, faithfulness, citation quality, latency, and workload. You will add local tracing, caching, context optimization, cost estimation, ACL-aware retrieval, tenant isolation, prompt-injection defenses, deletion workflows, CI/CD quality gates, and containerized deployment.

All mandatory demonstrations and Hands on Labs run locally with open-source tools. No paid AI API, cloud account, managed database, or dedicated GPU is required.

This course is designed for AI engineers, machine learning engineers, software developers, data engineers, platform engineers, solutions architects, MLOps professionals, and technical leads who already understand basic RAG concepts and want to build reliable enterprise AI systems.

Courze

Who this course is for:

AI engineers who want to design reliable, measurable, and secure enterprise RAG systems.
Machine learning engineers responsible for retrieval quality, evaluation, model routing, observability, and production performance.
Software and backend developers who have built a basic RAG application and want to move beyond simple vector-search demonstrations.
Data and platform engineers building document-ingestion, indexing, retrieval, evaluation, and deployment pipelines for generative AI applications.
Solutions architects and technical leads designing RAG platforms for regulated, permission-sensitive, multi-tenant, or business-critical environments.
MLOps, DevOps, QA, and security professionals responsible for evaluation gates, monitoring, authorization, governance, deployment, and operational controls.
Technical practitioners seeking hands-on skills in hybrid search, Self-RAG, Corrective RAG, GraphRAG, multimodal retrieval, and production readiness.