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Getting Started with Retrieve-Augmented Generation (RAG)

Chapter 1: Introduction to Retrieve-Augmented Generation

Limitations of Standard Large Language Models

What is Retrieve-Augmented Generation (RAG)?

The Core Architecture of a RAG System

RAG vs. Fine-tuning: Understanding the Differences

Benefits of Using RAG

Chapter 2: The Retrieval Component

Role of the Retriever in RAG

Introduction to Vector Embeddings

Common Embedding Models

Similarity Search: Finding Relevant Vectors

Introduction to Vector Databases

Choosing a Vector Database: Considerations

Practice: Generating Text Embeddings

Chapter 3: Preparing Data for Retrieval

Loading Documents from Various Sources

The Need for Document Chunking

Fixed-Size Chunking Strategies

Content-Aware Chunking Approaches

Metadata Association with Chunks

Storing Processed Data in a Vector Database

Hands-on Practical: Chunking Documents

Chapter 4: The Generation Component and Augmentation

Role of the Generator (LLM) in RAG

Structuring Prompts for RAG

Context Injection Methods

Managing Context Length Limitations

Generating the Final Response

Attributing Sources in Generated Output

Chapter 5: Building a Basic RAG Pipeline

Overview of RAG Frameworks (e.g., LangChain, LlamaIndex)

Setting up the Environment

Implementing the Retriever

Implementing the Generator Integration

Combining Retrieval and Generation

Running Queries Through the Pipeline

Hands-on Practical: End-to-End RAG System

Chapter 6: Evaluating and Improving RAG Systems

Challenges in Evaluating RAG

Component-Level Evaluation: Retrieval

Component-Level Evaluation: Generation

End-to-End RAG Evaluation Frameworks

Common Failure Modes

Basic Strategies for Improvement

Practice: Analyzing RAG Output Quality

Getting Started with Retrieve-Augmented Generation (RAG)

Prerequisites: Python & LLM concepts

Level: Intermediate

What You'll Learn

RAG Fundamentals
Understand the motivation behind RAG and its core architecture.
Information Retrieval
Grasp the role of the retriever component and techniques for finding relevant information.
Vector Embeddings and Databases
Learn how text is vectorized and stored for efficient retrieval.
Generation Augmentation
Understand how retrieved context is integrated with the LLM prompt.
Basic RAG Pipeline Implementation
Build a simple RAG system using standard libraries and models.
Data Preparation for RAG
Learn techniques for chunking and preparing documents for retrieval.

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