In the context of artificial intelligence (AI), chunks refer to semantically self-contained units of text that are used as the basic building blocks for information retrieval, indexing, and large language model (LLM) processing. These chunks typically range from a few sentences to a few hundred tokens and are designed to be topically coherent, fact-based, and aligned with a specific query intent. The concept of chunking has become increasingly relevant with the rise of AI-powered search interfaces like Google AI Mode, Perplexity, and ChatGPT Search.
Overview
Chunks are not arbitrary slices of text. Instead, they are deliberately segmented passages that capture a single idea, describe a specific entity, or explain a clear relationship between entities. These units are foundational in enabling AI systems to retrieve precise, contextually relevant content from vast corpora. As search evolves from keyword-matching to semantic understanding, the structure and quality of content chunks play a pivotal role in visibility.
Structure and Properties
A well-formed chunk is:
- Entity-centric: Focused on a particular product, concept, or named entity.
- Topically coherent: It addresses one core idea or question.
- Fact-based: Supports information needs with grounded, verifiable data.
- Self-contained: Can stand alone and still make sense without relying on external context.
- Semantically aligned: Matches user intent and expected sub-queries.
Chunks are often derived from the layout of web pages using layout-aware segmentation — for example, dividing text by headings, paragraphs, lists, and tables.
Role in AI Retrieval
Modern LLM-based systems, including Google’s AI Mode, retrieve chunks, not full pages. When a user query is submitted, the system breaks down the query into sub-questions and looks for corresponding chunks that match those intents with high semantic similarity. If a suitable chunk isn’t found on the page, the AI will often retrieve it from a competing site.
This retrieval strategy highlights the importance of chunk optimization — ensuring that all expected questions about a product or entity (e.g., price, materials, reviews, collection history) are clearly answered within distinct, well-structured chunks.
Use in E-commerce and AI Mode
In e-commerce, chunk optimization has become critical. Platforms like Google AI Mode are transforming product discovery into a conversational, multi-turn experience. AI agents pull product information directly from these chunks — so if your content isn’t chunked correctly, it may not surface at all.
WordLift has pioneered the use of multi-chunk embeddings within its Product Knowledge Graph. This allows AI agents to analyze and optimize product data, content, and internal linking structures at AI speed. For example, when evaluating a product detail page (PDP), the system checks if key sub-queries (such as price or customer reviews) align semantically with the content. If not, visibility in AI Mode may be compromised.
Further Reading
For more detailed examples and guidance, see:
Conclusion
Chunks are fundamental to how AI systems understand and retrieve information. As content discovery becomes more conversational and AI-driven, optimizing for chunks — and ensuring each chunk answers a clear, predictable query — is key to staying visible. Whether for e-commerce, editorial, or enterprise content, chunking is quickly becoming a core SEO and AI-readiness strategy.