AI Fundamentals — Chunking Strategies in RAG

At its core, an LLM (Large Language Model) takes an input and produces an output — but its answers come strictly from what it learned during training, not from your specific domain or private data.

RAG (Retrieval-Augmented Generation) bridges that gap by enabling the model to answer using up-to-date, domain-specific, and private information. A RAG-powered AI application searches a knowledge base first, pulls the most relevant information, and combines it with the user’s prompt to deliver a precise and contextually accurate response. In simple terms:

Without RAG — the AI returns a generic answer that may have no relevance to your context

With RAG — the AI searches your knowledge base, finds the best match, and feeds that to the LLM for a focused, accurate output

The three core steps of RAG are:

In this blog, we focus on chunking — one of the most critical components of any RAG application. Chunking is the process of breaking large text into smaller, meaningful pieces so an AI application can process and retrieve them efficiently.

We will walk through the core chunking strategies conceptually, and the next blog will cover hands-on implementation. To get the best results from a RAG pipeline, choosing the right chunking strategy matters. Several approaches are available, each suited to different document types and use cases.

The below table provides the comparison matrix for different Chunking Strategies.