Home Blog Learn

All Courses

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

Evaluating and Improving RAG Systems

Building a Retrieve-Augmented Generation (RAG) pipeline is a significant step, but determining its effectiveness and refining its performance are necessary follow-up actions. A RAG system that retrieves irrelevant information or generates inaccurate responses is of limited practical use.

This chapter focuses on the methods used to assess the quality of your RAG system. You will learn about:

Common difficulties encountered when evaluating RAG outputs.
Approaches to evaluate the individual components: the retriever's ability to find relevant context and the generator's ability to produce faithful and relevant answers based on that context.
Metrics used for assessing retrieval (like Hit Rate or Mean Reciprocal Rank, $MRR$ ) and generation quality.
Identifying typical failure points in RAG systems.
Basic techniques to enhance performance, such as adjusting data chunking strategies or refining prompts.

By the end of this chapter, you will have a foundational understanding of how to measure the performance of your RAG system and apply initial strategies for improvement.

Sections

6.1 Challenges in Evaluating RAG
6.2 Component-Level Evaluation: Retrieval
6.3 Component-Level Evaluation: Generation
6.4 End-to-End RAG Evaluation Frameworks
6.5 Common Failure Modes
6.6 Basic Strategies for Improvement
6.7 Practice: Analyzing RAG Output Quality

© 2025 ApX Machine Learning

;