A Retrieval-Augmented Generation (RAG) system's intelligence comes from the data it can access. But you can't simply point a Large Language Model (LLM) at a library of raw documents and expect good results. When a user asks a specific question, a 300-page PDF is too much noise and not enough signal. The LLM needs focused, relevant information to generate a useful answer.

This is where data ingestion and chunking come in. The goal is to break down large, unstructured documents into smaller, self-contained pieces of information. Each chunk should be just big enough to contain a complete idea, making it easier for the system to find the exact snippet of text that answers a user's query. Think of it as creating a detailed index for a book that doesn't have one.

The simplest way to chunk text is with Recursive Character Splitting This method attempts to split text based on a prioritized list of separators. It starts with the most logical separator, like a double newline (\n\n) which often separates paragraphs. If a resulting chunk is still too large, it moves to the next separator in the list, like a single newline (\n), and then spaces ( ), until each chunk is under a specified size limit.

While effective, recursive splitting is agnostic to the content's meaning. A more advanced approach is Semantic Chunking It uses language models to analyze the relationships between sentences, breaking the text at points where the topic shifts. Instead of splitting in the middle of a cohesive thought, it groups semantically related sentences together. This creates chunks that are more contextually complete and often yield better results during retrieval.

The optimal chunking strategy often depends on the structure of the source document. A one-size-fits-all approach rarely works best.

For example, with Markdown files, splitting by header levels (#, ##, etc.) is a natural way to create chunks based on the document's own hierarchy. For HTML, you can split by tags like <div>, <p>, or <table> to keep related content together. When dealing with PDFs, a simple and effective strategy is to treat each page as a separate chunk, though this can be problematic if sentences or paragraphs span across page breaks.

Two key parameters govern any chunking strategy: chunk size and overlap.

Chunk size determines the maximum length of each chunk (often measured in characters or tokens). There's a fundamental trade-off here. Smaller chunks are more precise and less noisy, which can lead to better retrieval for highly specific queries. However, they risk losing important surrounding context. Larger chunks retain more context but might dilute the relevance of the core information, making it harder for the model to pinpoint the exact answer.

Chunk overlap helps mitigate the problem of losing context at the boundaries. This technique makes the end of one chunk the beginning of the next one. For instance, if you have a chunk size of 1000 characters and an overlap of 150, the first chunk will be characters 0-1000, the second will be characters 850-1850, and so on. This ensures that a sentence or idea split at the end of a chunk is fully present in the next one. A typical overlap is between 10-20% of the chunk size.

Before you can test your knowledge, it's important to remember one final step: preprocessing. This involves cleaning your source data by removing irrelevant elements like boilerplate headers, footers, ads, or navigation menus from web pages. Clean, focused content leads to clean, focused chunks.

Mastering data ingestion and chunking is the first and most critical step in building a high-performing RAG system. The quality of your chunks directly impacts the quality of the answers your system can provide.