Chunking (Content)

Chunking is a critical but often overlooked step in how AI systems process and retrieve web content. The way your content is divided into chunks directly determines whether AI answer engines can find, understand, and cite the specific information users are searching for.

How Chunking Works in AI Systems

The Retrieval Pipeline

Before an AI system can generate an answer, it must retrieve relevant information. Most documents are too long to process as a single unit, so they are broken into smaller chunks that can be individually indexed, searched, and retrieved.

The Process:

1. A web page or document is crawled and ingested
2. The content is split into smaller segments (chunks)
3. Each chunk is converted into a vector embedding
4. Embeddings are stored in a vector database
5. At query time, the most relevant chunks are retrieved
6. Retrieved chunks are passed to the LLM for answer generation

Chunking Strategies

Strategy	Description	Best For
Fixed-size	Split text into equal-length segments (e.g., 500 tokens)	Simple implementation, uniform processing
Sentence-based	Split at sentence boundaries	Preserving grammatical completeness
Paragraph-based	Split at paragraph breaks	Maintaining topical coherence
Semantic	Split where the topic or meaning shifts	Best retrieval accuracy
Heading-based	Split at section headings (H2, H3)	Well-structured articles and documentation
Hybrid	Combine multiple strategies	Balancing accuracy and coverage

Why Chunk Size Matters

Too Small

When chunks are too small, they lose context. A single sentence like “This increased revenue by 40%” is meaningless without knowing what “this” refers to. Small chunks lead to:

• Lost context and ambiguous references
• Incomplete information in retrieved results
• Higher chance of misinterpretation by the LLM
• Fragmented citations

Too Large

When chunks are too large, retrieval precision drops. A 3,000-word chunk may contain the answer to a query but also contain large amounts of irrelevant information. Large chunks cause:

• Diluted relevance scores
• Wasted context window space
• Slower processing times
• Less precise citations

The Optimal Range

Most AI retrieval systems work best with chunks between 200 and 800 tokens, though the optimal size depends on the content type and the retrieval system being used.

Content Type	Recommended Chunk Size	Rationale
FAQ pages	100-200 tokens	Each Q&A pair is self-contained
Blog articles	300-500 tokens	Paragraphs with complete thoughts
Technical docs	400-600 tokens	Sections with enough detail
Research papers	500-800 tokens	Complex arguments need more context

How Content Structure Affects Chunking

Heading Hierarchy

AI systems frequently use HTML heading tags (H1, H2, H3) as natural chunk boundaries. A well-structured article with clear heading hierarchy produces better chunks than a wall of text.

Well-Structured (Good Chunking):

• Each H2 section covers one distinct subtopic
• H3 subsections provide logical sub-divisions
• Paragraphs within sections are self-contained

Poorly Structured (Bad Chunking):

• Long sections covering multiple topics
• No clear heading hierarchy
• Paragraphs that depend heavily on surrounding context

Self-Contained Paragraphs

Writing paragraphs that are self-contained and do not rely heavily on surrounding text for meaning produces better chunks. Each paragraph should ideally answer a specific question or make a complete point.

Example of a well-chunked paragraph:

Cosine similarity is a mathematical measure used by AI search systems to determine how closely related two pieces of text are. It works by converting text into numerical vectors and measuring the angle between them, where a smaller angle (closer to 1.0) indicates higher similarity.

This paragraph stands alone as a complete, useful piece of information.

Optimizing Content for AI Chunking

1. Use Clear Section Breaks

Structure content with descriptive headings that signal topic changes. Each section should be a self-contained unit of information that makes sense even when extracted from the full document.

2. Front-Load Key Information

Place the most important information at the beginning of each section. AI chunking systems that split at heading boundaries will capture the key points even if a section is truncated.

3. Define Terms Inline

Do not assume the reader has read previous sections. When referencing a concept, provide enough context within each section so that any individual chunk can be understood independently.

4. Use Lists and Tables Strategically

Structured data formats like lists and tables create natural, information-dense chunks that are easy for AI systems to parse and retrieve.

5. Avoid Orphaned References

Minimize the use of pronouns and references that depend on other parts of the document. Instead of “As mentioned above,” restate the relevant information.

Chunking and Overlap

Sliding Window Approach

Many retrieval systems use overlapping chunks, where each chunk shares some text with the previous and next chunks. This overlap ensures that information at chunk boundaries is not lost.

Example with 20% overlap:

• Chunk 1: Sentences 1-10
• Chunk 2: Sentences 8-18
• Chunk 3: Sentences 16-26

This approach preserves context at boundaries but increases storage and processing requirements.

Why It Matters for AEO

Chunking is the bridge between your content and AI-generated answers. When a user asks a question and an AI answer engine retrieves information to generate its response, it is not retrieving your entire page. It is retrieving specific chunks that matched the query.

If your content is structured so that each section is a clear, self-contained, information-rich chunk, your content is more likely to be retrieved and cited accurately. Poorly structured content with ambiguous references, long unfocused sections, and buried key information will be chunked poorly, reducing your chances of appearing in AI-generated answers. Optimizing for chunking means writing content that is modular, clearly structured, and contextually complete at every level.