Welcome to RAG Systems! This course will teach you how to build Retrieval Augmented Generation systems - one of the most powerful architectures for creating AI applications that can access and reason over custom knowledge bases.

What is RAG?

Retrieval Augmented Generation (RAG) is an architectural pattern that enhances Large Language Models by retrieving relevant information from external knowledge sources before generating responses. Instead of relying solely on the model's training data, RAG systems dynamically fetch contextual information to provide more accurate, up-to-date, and grounded answers.

The seminal paper "Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks" by Lewis et al. (2020) introduced RAG as a method that combines parametric memory (the LLM's trained weights) with non-parametric memory (external documents retrieved at inference time).

Motivation: Why RAG?

Large Language Models have revolutionized NLP, but they face several critical limitations that RAG addresses:

• Knowledge Cutoff - LLMs are frozen in time. Their knowledge ends at the training cutoff date, making them unable to answer questions about recent events without external data sources.
• Hallucinations - LLMs may confidently generate plausible-sounding but incorrect information. RAG grounds responses in retrieved facts, significantly reducing false information.
• Domain Specificity - Generic LLMs lack specialized knowledge. RAG enables domain expertise without expensive fine-tuning by providing specialized documents at query time.
• Transparency & Citability - RAG systems can show sources and citations for generated content, enabling fact-checking and building user trust.
• Privacy & Security - Sensitive organizational data remains in your control, not baked into model weights. You can update, remove, or restrict access to specific documents.
• Cost Efficiency - Fine-tuning LLMs is expensive and requires retraining when knowledge changes. RAG allows dynamic knowledge updates without model retraining.

The RAG Pipeline: End-to-End Flow

A production RAG system consists of two main phases: an offline ingestion phase and an online query phase.

Phase 1: Ingestion (Offline/Asynchronous)

This phase prepares documents for efficient retrieval:

1. Document Loading - Extract content from PDFs, web pages, databases, APIs, and other sources
2. Preprocessing - Clean text, remove boilerplate, handle special characters
3. Chunking - Split documents into semantically meaningful segments (covered in detail later)
4. Metadata Extraction - Extract or assign metadata (title, date, source, category) for filtering
5. Embedding Generation - Convert text chunks into dense vector representations using embedding models
6. Indexing - Store embeddings in a vector database with appropriate indices for fast similarity search

Phase 2: Query (Online/Per Request)

This phase handles user queries in real-time:

1. Query Understanding - Parse and understand user intent, optionally rewrite or expand queries
2. Query Embedding - Convert the query into a vector using the same embedding model
3. Retrieval - Find the most similar document chunks using vector similarity search
4. Re-ranking (Optional) - Reorder retrieved documents using a more sophisticated model
5. Context Augmentation - Combine retrieved context with the user query in a prompt
6. Generation - LLM generates a response based on the augmented prompt
7. Post-processing - Add citations, validate against retrieved context, format response

Core Components of RAG

A typical RAG system consists of several key components that work together:

• Document Loaders - Handle various formats: PDF, HTML, Markdown, DOCX, databases, APIs
• Text Splitters - Chunk documents using strategies like fixed-size, recursive, semantic, or sentence-based splitting
• Embedding Models - Convert text to vectors (OpenAI Ada, Cohere, Sentence-Transformers, BGE, E5)
• Vector Stores - Store and index embeddings (Pinecone, Weaviate, Qdrant, ChromaDB, Milvus, FAISS)
• Retrievers - Find relevant documents using semantic, lexical, or hybrid search
• Re-rankers - Cross-encoder models that reorder results (Cohere Rerank, BGE Reranker, ColBERT)
• LLM - Generate responses using retrieved context (GPT-4, Claude, Gemini, Llama, Mistral)
• Orchestrator - Coordinate the entire pipeline (LangChain, LlamaIndex, Haystack)

RAG vs. Fine-Tuning: When to Use Which?

Understanding when to use RAG versus fine-tuning is crucial for building effective AI systems:

Use RAG When:

• Knowledge needs to be updated frequently (news, documentation, product catalogs)
• You need source attribution and citations for compliance or trust
• The knowledge base is too large to fit in context or training data
• You want to control access to specific documents dynamically
• Budget and time constraints prevent fine-tuning
• You need to combine multiple specialized knowledge sources

Use Fine-Tuning When:

• You need to change the model's behavior, style, or format consistently
• Knowledge is relatively static and well-defined
• Low latency is critical and retrieval overhead is unacceptable
• You want the model to learn specialized reasoning patterns

Best Practice: Combine Both

In many production systems, the optimal approach combines both techniques: fine-tune for style/format/reasoning, then use RAG for dynamic knowledge retrieval.

RAG Variants and Patterns

The RAG ecosystem has evolved with several advanced patterns:

• Naive/Vanilla RAG - Basic retrieve-then-generate approach. Simple but effective baseline.
• Advanced RAG - Incorporates query rewriting, hybrid search, re-ranking, and metadata filtering for improved precision and recall.
• Modular RAG - Combines multiple retrieval strategies (lexical + semantic + knowledge graph) with flexible orchestration.
• Agentic RAG - Uses AI agents to dynamically decide when and how to retrieve, enabling multi-step reasoning and tool use.
• Self-RAG - The model self-reflects on whether retrieval is needed and evaluates retrieved document relevance.
• Corrective RAG (CRAG) - Evaluates retrieval quality and takes corrective actions if retrieved documents are irrelevant.
• RAPTOR - Recursive Abstractive Processing for Tree-Organized Retrieval. Builds hierarchical summaries for multi-level retrieval.
• HyDE (Hypothetical Document Embeddings) - Generates a hypothetical answer first, then uses it for retrieval instead of the original query.

Agentic RAG: The Next Evolution

Agentic RAG represents a significant evolution in RAG architectures, where an LLM acts as an intelligent agent that orchestrates retrieval dynamically:

• Query Analysis - The agent analyzes whether the query requires retrieval at all
• Source Selection - Chooses which knowledge sources to query (internal docs, web search, databases)
• Iterative Retrieval - Can retrieve multiple times, refining queries based on initial results
• Multi-Step Reasoning - Breaks complex questions into sub-questions, retrieving for each
• Tool Integration - Can call other tools (calculators, APIs) alongside retrieval

This approach is particularly powerful for complex, multi-hop questions that require synthesizing information from multiple sources.

RAG vs. Long Context Windows

With models supporting 100K+ token contexts, you might wonder if RAG is still relevant. The answer is yes, for several reasons:

• Computational Cost - Long context inference is expensive. RAG with targeted retrieval is more cost-efficient.
• Lost in the Middle - Research shows LLMs struggle with information in the middle of long contexts. RAG places relevant info at the start.
• Latency - Processing 100K tokens is slower than retrieving a focused 2K context.
• Scalability - RAG scales to millions of documents; context windows don't.
• Needle in a Haystack - RAG is more reliable at finding specific information than stuffing everything in context.

That said, long context windows complement RAG by allowing more retrieved chunks to be included in the prompt.

Real-World Applications

RAG powers numerous production applications across industries:

• Customer Support - AI chatbots with access to product documentation, FAQs, and support history
• Documentation Search - Intelligent technical documentation assistants (GitHub Copilot Chat, Stripe Docs)
• Legal Research - Finding relevant cases, regulations, and precedents from vast legal databases
• Medical Diagnosis Support - Assisting healthcare professionals with medical literature and guidelines
• Financial Analysis - Analyzing earnings reports, SEC filings, and market research
• Research Assistants - Helping researchers find and synthesize relevant academic papers
• Enterprise Search - Semantic search across company documents, Slack, email, and knowledge bases
• Code Assistants - Retrieving relevant code snippets, documentation, and examples from codebases

Popular RAG Frameworks

Several frameworks simplify RAG development:

• LangChain - Comprehensive framework with extensive integrations for document loaders, vector stores, and LLMs
• LlamaIndex - Specialized for data ingestion, indexing, and advanced retrieval strategies
• Haystack - Production-ready NLP framework with focus on search and QA pipelines
• Semantic Kernel - Microsoft's AI orchestration framework with enterprise features
• DSPy - Declarative framework for programmatic optimization of LLM pipelines
• Vercel AI SDK - Lightweight SDK for building AI applications with RAG support

What You'll Learn in This Course

This comprehensive course covers the full RAG development lifecycle:

• RAG Architecture - Deep dive into components, data flow, and design patterns
• Document Processing - Chunking strategies (naive, semantic, late chunking), metadata extraction, and preprocessing
• Embeddings - Word vs. sentence embeddings, model selection, and optimization
• Retrieval Strategies - Lexical (BM25), semantic, and hybrid retrieval with score fusion
• Re-ranking - Cross-encoders, bi-encoders, ColBERT, and learning-to-rank
• Query Enhancement - Query rewriting, expansion, HyDE, and multi-query strategies
• Advanced Techniques - Contextual retrieval, sentence-window retrieval, auto-merging
• Evaluation - Retrieval metrics (precision, recall, MRR) and generation metrics (faithfulness, relevance)
• Production Optimization - Caching, batching, async processing, and cost optimization
• Multimodal RAG - Handling images, tables, and other non-text content

Prerequisites

• Understanding of Large Language Models (or take our LLM course first)
• Basic knowledge of vector databases and embeddings (or take our Vector DB course)
• Python programming experience
• Familiarity with APIs and basic web development

Key Takeaways

By the end of this course, you'll be able to:

• Design and implement production-grade RAG architectures
• Choose appropriate chunking, embedding, and retrieval strategies for your use case
• Evaluate and optimize RAG systems using industry-standard metrics
• Build end-to-end RAG applications with proper error handling and observability
• Deploy and scale RAG systems in production environments

Let's build powerful RAG systems!