Advanced RAG Architecture: Production AI Systems

Master Retrieval-Augmented Generation (RAG), Vector Databases, Embeddings, Reranking, and Enterprise AI Pipelines

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Created byMeta Brains

Last updated 7/2026

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What you'll learn

Master advanced Retrieval-Augmented Generation (RAG) architectures for building accurate, production-ready AI applications.
Implement hybrid search, reranking, query transformation, and advanced retrieval strategies to improve LLM performance.
Build scalable RAG pipelines using vector databases, embeddings, chunking, indexing, and metadata filtering techniques.
Optimize, evaluate, debug, and deploy enterprise-grade RAG systems for chatbots, knowledge assistants, and AI applications.

Course content

7 sections • 40 lectures • 5h 2m total length

What is Retrieval-Augmented Generation (RAG)7:38
Limitations of Basic RAG Systems7:55
Explore the limitations of basic rag systems, including retrieval irrelevance, chunking context loss, and limited multi-step reasoning, amplified by memory gaps and source redundancy across policy documents.
Enterprise Use Cases for Advanced RAG6:14
Modern RAG Architecture Overview7:26
Explore modern retrieval augmented generation architecture that coordinates data ingestion, processing, indexing, and query processing to deliver grounded, accurate answers with hybrid retrieval and context construction.
Components of a Production RAG Pipeline8:35
Orchestrate a production rag pipeline that cleans data, chunks documents, embeds chunks into a vector database, and uses retriever, re-ranker, and prompt builder to deliver answers from trusted external knowledge.
Development Environment and Tools Setup8:37

Understanding Embeddings and Vector Databases6:26
Explain how embeddings convert text and other content into vectors, how vector databases store and search these embeddings, and how semantic similarity enables retrieval augmented generation.
Chunking Strategies (Fixed, Recursive, Semantic)22:27
Semantic Chunking Best Practices8:09
Hybrid Search (Vector + Keyword Search)5:49
Re-ranking Techniques for Better Results5:09
Metadata Filtering and Context Optimization6:48

Query Preprocessing Techniques8:04
Multi-Query Generation Strategies7:11
Query Rewriting with LLMs8:06
HyDE (Hypothetical Document Embeddings)7:18
Explore HyDE, the hypothetical document embeddings approach, which uses a generated document to create embeddings before search, improving retrieval and relevance in RAG systems.
Context Expansion Techniques5:23
Discover context expansion techniques in retrieval augmented generation, enriching retrieved content with neighboring chunks, metadata, and conversation history to improve accuracy, grounding, and efficiency.
Handling Ambiguous and Complex Queries4:14
Master advanced RAG systems by applying context-aware interpretation, query expansion, semantic understanding, and multi-step reasoning to resolve ambiguity and handle multi-intent queries with accurate retrieval and complete responses.

Self-RAG Architecture and Workflows21:37
Explore self-RAG architecture, a self-reflective retrieval augmented generation framework that dynamically decides retrieval, uses reflection tokens, and grounds answers in evidence through document grading, query rewriting, and answer reflection.
Corrective RAG (CRAG) Implementation5:40
Graph RAG Concepts and Knowledge Graphs4:40
Multimodal RAG (Text, Images, Tables)8:10
Agentic RAG Workflows5:20
Multi-Step Retrieval Pipelines5:14
Explore multi step retrieval pipelines that transform queries, rank and refine retrieved documents, and combine hybrid methods to boost accuracy, relevance, and efficiency in production ai systems.

Setting Up an Enterprise RAG System12:05
Retrieval and Generation Evaluation8:32
Examine a production rag architecture with retrieval and generation evaluation, processing PDFs and knowledge bases using gpt-4 mini, embeddings, and metrics like mrr and ndcg.
Query Optimization8:08
Observability, Logging, and Monitoring5:56
Data Governance and Compliance6:28
Security and Access Control for Enterprise Data7:35

Scaling Large-Scale RAG Systems9:10
Optimizing Latency and Throughput6:03
Caching and Response Optimization9:38
Learn how multi-layer caching in a RAG system—embedding, retrieval, re-ranking, prompt, and response caches—reduces latency and unnecessary work, with semantic caches and cache-aside patterns guiding freshness.
Enterprise Knowledge Base Integration3:46
End-to-End Enterprise RAG Project5:25
Common Pitfalls and Best Practices6:00

Requirements

Basic understanding of Python and Large Language Models (LLMs) is recommended. Familiarity with AI concepts is helpful but not required. All advanced RAG concepts are explained step by step.

Description

Disclaimer : This course contains the use of artificial intelligence.

Artificial Intelligence is rapidly evolving, and Retrieval-Augmented Generation (RAG) has become one of the most important techniques for building reliable, accurate, and scalable AI applications. Instead of relying solely on the knowledge stored inside a Large Language Model (LLM), RAG enables AI systems to retrieve relevant information from external data sources, significantly improving response quality while reducing hallucinations.

In this comprehensive course, you'll learn advanced Retrieval-Augmented Generation techniques used in modern AI products and enterprise-grade applications. Starting with the foundations of RAG, you'll progressively build sophisticated retrieval pipelines that deliver fast, accurate, and context-aware responses.

Throughout the course, you'll explore vector databases, embeddings, semantic search, document chunking, indexing strategies, metadata filtering, hybrid search, query transformation, reranking models, prompt engineering for RAG, retrieval optimization, evaluation methods, and performance tuning. You'll also discover how to design scalable RAG architectures capable of handling real-world business use cases.

Rather than focusing only on theory, this course emphasizes practical implementation and industry best practices. You'll understand how modern AI assistants, enterprise search engines, knowledge management systems, document question-answering platforms, and intelligent chatbots are built using advanced RAG techniques.

By the end of this course, you'll have the knowledge and confidence to design, optimize, evaluate, and deploy production-ready Retrieval-Augmented Generation systems that integrate seamlessly with today's leading Large Language Models.

Whether you're an AI engineer, machine learning practitioner, Python developer, data scientist, or GenAI enthusiast, this course will provide the advanced skills needed to build the next generation of intelligent AI applications.

Who this course is for:

AI engineers, Python developers, data scientists, machine learning practitioners, GenAI enthusiasts, and anyone looking to build advanced, production-ready Retrieval-Augmented Generation (RAG) applications using modern AI techniques.

Advanced RAG Architecture: Production AI Systems

What you'll learn

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Course content

Introduction to Advanced RAG Techniques6 lectures • 46min

Advanced Retrieval and Search Strategies6 lectures • 55min

Project : Query Enhancement and Retrieval Optimization6 lectures • 40min

Advanced RAG Architectures6 lectures • 51min

Project : Production-Ready Enterprise RAG System6 lectures • 49min

Performance Optimization and Enterprise Projects6 lectures • 40min

Conclusion and Real World Scenarios4 lectures • 22min

Requirements

Description

Who this course is for: