Chunking Strategies for Retrieval-Augmented Generation (RAG) Systems

About the Course

Chunking is one of the most critical design decisions when building Retrieval-Augmented Generation (RAG) systems and document retrieval pipelines. The way documents are divided into smaller segments directly determines how effectively information can be indexed, retrieved, and interpreted by large language models.

Poor chunking can lead to missing context, irrelevant retrieval results, and unreliable responses—even if the rest of the AI system is well designed.

This course explores chunking as an architectural component of AI systems, not just a preprocessing step.

You will learn how different chunking strategies work, how they affect retrieval accuracy, and how to choose the best approach based on document structure, dataset characteristics, and application requirements.

The course covers a wide range of chunking techniques including:

• Fixed-size chunking

• Sentence-based chunking

• Sliding window chunking

• Semantic chunking

• Structure-aware chunking for documents such as PDFs, HTML, Markdown, tables, and code

By the end of this course, you will be able to design robust chunking pipelines that improve retrieval performance and build reliable AI systems that depend on document-based knowledge.

Course Objectives

By the end of this course you will be able to:

• Understand why chunking is a foundational part of retrieval-based AI systems

• Identify factors that influence chunking strategies across datasets and tasks

• Apply multiple chunking techniques depending on content type

• Diagnose retrieval problems caused by poor chunking design

• Compare chunking strategies and understand their trade-offs

• Design hybrid chunking pipelines for complex document structures

• Evaluate chunking strategies using measurable metrics

Prerequisites

• Basic Python programming knowledge

• Familiarity with Natural Language Processing concepts

• Basic understanding of embeddings and vector databases

• Introductory knowledge of LLM-based applications

Who This Course Is For

This course is ideal for:

• AI engineers building Retrieval-Augmented Generation systems

• ML engineers working on document retrieval pipelines

• Backend developers integrating LLM-powered applications

• Data scientists working with large document collections

• Developers improving AI-driven search and knowledge systems