How to Chunk Documents for Better AI Understanding

Why Chunking Matters

When someone asks your chatbot a question, the RAG system searches for the most relevant chunks and sends them to the AI model along with the question. The AI can only use the information in the chunks it receives. If the relevant information is buried inside a huge chunk of unrelated text, the AI has to sift through noise. If the information is split across two chunks at an awkward boundary, the AI gets an incomplete picture.

Good chunking means each piece is focused on one idea or topic, contains enough context to make sense on its own, and does not split important information across boundaries. The platform's automatic chunking does a good job with well-structured content, but you can improve results by organizing your source documents with chunking in mind.

How Automatic Chunking Works

The platform splits your text at natural boundaries: paragraph breaks, section headings, and sentence endings. It aims for chunks between 250 and 2,000 characters. The algorithm prefers to split at paragraph breaks (double line breaks), then at sentence boundaries, and only splits mid-sentence as a last resort for very long paragraphs.

Each chunk is then sent to the embedding model independently. The embedding captures the meaning of that specific chunk, and the chunk is stored alongside its embedding for retrieval.

Writing for Better Chunks

You cannot control exactly where the chunking algorithm splits your text, but you can write content that chunks well naturally:

Use Clear Paragraph Breaks

Separate topics with blank lines. Each paragraph should cover one point. The chunking algorithm uses paragraph breaks as preferred split points, so well-separated paragraphs produce well-focused chunks.

Keep Paragraphs Self-Contained

Each paragraph should make sense on its own. Avoid starting a paragraph with "As mentioned above" or "Continuing from the previous point." The AI may see this chunk without the preceding one, so it needs enough context to understand the information independently.

Put Related Information Together

If your return policy has three conditions (time limit, original packaging, receipt required), put all three in the same paragraph or adjacent short paragraphs. If they are scattered across different sections of a long document, they may end up in different chunks, and the chatbot might only retrieve one or two conditions when answering.

Use Headings to Signal Topic Changes

Section headings help the chunking algorithm recognize topic boundaries. A heading followed by content is more likely to produce a clean chunk than continuous text that transitions between topics without a visible break.

Chunk Size and Its Effect on Quality

Small Chunks (250 to 500 characters)

More focused and precise in search results. Each chunk covers one specific point. Good for FAQ-style content with short, direct answers. The downside is that complex topics may be split across many chunks, requiring more chunks to be retrieved for a complete answer.

Medium Chunks (500 to 1,000 characters)

The sweet spot for most business content. Large enough to provide context, small enough to be focused. A typical paragraph or short section produces a medium chunk that works well for both search precision and answer completeness.

Large Chunks (1,000 to 2,000 characters)

Provide more context per chunk but may include some off-topic information. Good for complex topics where the answer requires multiple related facts. The downside is less precise search matching, since the embedding represents the average meaning of a larger text span.

Common Chunking Problems

Important information split across chunks

If pricing details end up in one chunk and the product name is in the previous chunk, the AI might retrieve the pricing without knowing which product it refers to. Fix: write pricing information with the product name included in the same paragraph.

Chunks with mixed topics

A chunk that discusses both shipping and returns may be retrieved for either topic, adding irrelevant information to responses about the other. Fix: separate these into distinct paragraphs with clear breaks between them.

Very short chunks with no context

A chunk that just says "$29.99 per month" has no context for the embedding model to capture meaning. Fix: write complete sentences that include what the price is for ("The Widget Pro Basic plan costs $29.99 per month and includes up to 1,000 transactions").