How AI Systems Actually Read Your Content (And What SEOs Need to Do About It)

Most SEOs are still optimizing content the way they did five years ago: targeting word counts, tweaking meta descriptions, and hoping Google’s crawler picks up what they’re putting down. But the systems deciding whether your content shows up in AI Overviews, ChatGPT responses, Perplexity answers, and Claude’s citations don’t read your page the way a human does. They don’t even read it the way a traditional search crawler does.

They chunk it.

Understanding how that process works, and structuring your content to align with it, is one of the most significant and least-discussed opportunities in SEO right now.

What Chunking Is and Why It Matters for Search Visibility

When an AI system ingests a web page for retrieval-augmented generation (RAG), it doesn’t process the entire document as a single unit. Instead, the content gets broken into smaller segments called “chunks.” Each chunk gets converted into a numerical representation (an embedding), stored in a vector database, and retrieved later when a user asks a question that’s semantically related.

Here’s the part that matters for SEOs: the quality of those chunks directly determines whether your content gets retrieved. A chunk that contains a single, coherent thought produces a clean embedding that matches well against relevant queries. A chunk that splits mid-paragraph, mixes two unrelated ideas, or contains filler text produces a diluted embedding that’s less likely to surface for anything.

In other words, the atomic unit of AI search visibility isn’t the page. It’s the chunk.

There Is No Universal Chunk Size

One of the most common misconceptions in early AEO (Answer Engine Optimization) discussions is that there’s a single “correct” chunk size. There isn’t. Different platforms use different defaults, and many allow configuration. Here’s what the major platforms actually do:

OpenAI defaults to 800-token chunks with 400-token overlap between consecutive chunks in their Retrieval API. Their system allows configuration between 100 and 4,096 tokens per chunk. (Source: OpenAI Retrieval API Documentation)

Google Vertex AI Search defaults to 500-token chunks, with a configurable range of 100 to 500 tokens. Their system also supports heading-aware chunking through their layout parser, which considers document structure when deciding where to split. (Source: Google Cloud Documentation - Parse and Chunk Documents)

Amazon Bedrock Knowledge Bases defaults to approximately 300-token chunks while preserving sentence boundaries. Their platform also offers hierarchical chunking (recommended parent size of 1,500 tokens, child size of 300 tokens) and semantic chunking based on embedding similarity. (Source: AWS Bedrock Documentation - Content Chunking)

Microsoft Azure AI Search recommends starting with 512-token chunks and 128 tokens of overlap (25%), noting that content type should influence the final configuration. (Source: Microsoft Learn - Chunk Documents)

Cohere Embed v3 supports a maximum of 512 tokens per chunk for their embedding model. Their newer Embed v4 supports up to 128K tokens, potentially reducing the need for aggressive chunking in some architectures. (Source: Cohere Embed Documentation)

LangChain, the most widely used open-source RAG framework, provides a RecursiveCharacterTextSplitter that defaults to splitting by character count using paragraph, sentence, and word boundaries in that order. It doesn’t prescribe a single default chunk size, but common implementations use 256 to 1,024 tokens. (Source: LangChain Text Splitter Documentation)

The practical range across these systems is roughly 256 to 800 tokens, with most clustering around 300 to 512. That’s the window SEOs should be thinking about when structuring content.

How Chunks Actually Get Created: Fixed Blocks, Not a Rolling Window

A common question is whether chunking works like a sliding window that moves one token at a time across your content, or whether it creates discrete blocks. The answer is neither exactly. It’s fixed blocks with overlap.

Using a 500-token chunk size with 100-token overlap as an example, the system creates:

• Chunk 1: tokens 1-500
• Chunk 2: tokens 401-900
• Chunk 3: tokens 801-1300
• Chunk 4: tokens 1201-1700

Each chunk is a discrete unit that gets its own embedding. But the last portion of one chunk is repeated as the opening of the next. That overlap zone is the buffer that preserves context at boundaries. Think of it like shingles on a roof: each shingle is its own unit, but it overlaps slightly with the ones next to it so nothing falls through the gaps.

A true rolling window (1-500, 2-501, 3-502…) would be computationally absurd, generating thousands of near-identical embeddings for a single page. Nobody does that. And pure sequential blocks with no overlap (1-500, 501-1000) would risk splitting a thought at the exact boundary with no context bridge.

The overlap percentages vary by platform. OpenAI’s default of 400-token overlap on 800-token chunks is notably aggressive: 50% of each chunk is shared with its neighbors. Amazon Bedrock recommends 20% overlap. Azure suggests 25%.

This has a critical implication for content structure: if your thought fits cleanly within a single chunk, the overlap is just insurance. If your thought spans two chunks, you’re relying on that overlap zone to carry the bridge, and it may not preserve the full meaning. That’s why self-contained sections matter so much. You want each idea to land within a single block rather than depending on the overlap to hold it together.

It also means that redundant or repetitive content creates even more noise in the embedding space. If two paragraphs on your page say essentially the same thing, overlap ensures those near-duplicate ideas get embedded multiple times across multiple chunks, all competing with each other during retrieval and none of them being the definitive match.

This is what I call chunk deduplication as a content strategy. Each distinct section of your page should contain a unique, singular thought. Redundant content doesn’t just waste space on the page; it actively degrades your content’s retrievability by creating noisy, competing embeddings.

Semantic-Aware Content Architecture

Understanding chunk mechanics leads to a practical content architecture framework. Instead of thinking about page structure purely in terms of UX or traditional SEO hierarchy, consider how your content will be parsed by these systems:

One complete thought per section. Each heading-bounded section should contain a self-contained idea that would make sense to a reader (or an AI system) without needing the surrounding context. This is the single most impactful structural change you can make. If a section requires reading the previous section to make sense, the resulting chunk will produce a weaker embedding.

Keep sections within the token window. A section that runs 800 or 1,000 tokens will almost certainly get split across chunks by most systems. When that happens, the split point is determined by the chunking algorithm, not by the logic of your content. Aim for sections in the 200 to 400 token range (roughly 150 to 300 words) so that each section lands in a single chunk regardless of which platform is processing it.

Use descriptive headings that provide context. Several chunking systems (notably Google’s layout parser) prepend heading context to chunks. A heading like “Benefits” tells the AI system almost nothing. A heading like “Why Semantic Chunking Improves AI Search Retrieval” gives the chunk its own context, improving the embedding quality even when the chunk is processed in isolation.

Front-load the key claim. Within each section, put your most important statement first. If a system chunks your content and the overlap captures only the first few sentences of the next section, you want those sentences to carry weight. The inverted pyramid isn’t just a journalism convention; it’s now an AI optimization principle.

The Freshness Signal: What’s Actually Happening

There’s a common observation that AI systems favor recent content. This is true, but the mechanism is often misunderstood.

In conversational AI (ChatGPT, Claude, etc. during a chat session), recent context within the conversation window naturally receives more attention due to how transformer attention mechanisms work. This is a property of the model architecture, not a ranking signal. It means something you mentioned earlier in a conversation carries less weight than what you just said, but it has nothing to do with how your web content ranks.

For search-grounded AI answers (Google AI Overviews, Perplexity, ChatGPT with web browsing), the freshness signal comes from the search layer, not the LLM itself. Google has long weighted content freshness in its search index, and AI Overviews inherit that signal. Perplexity and ChatGPT with browsing both rely on real-time search results where publication and modification dates influence ranking.

The practical implication is the same either way: keep your content fresh. Regularly update publish dates, refresh statistics and examples, and ensure your structured data reflects accurate modification timestamps. But understand that you’re optimizing for the search retrieval layer, not manipulating the AI model directly.

Building a Content Audit Around These Principles

This framework translates directly into an actionable audit methodology. For any page you want to optimize for AI retrieval:

Assess section coherence. Does each heading-bounded section contain exactly one complete thought? Could you extract that section in isolation and have it still make sense?

Check section length. Are your sections staying within the 200-to-400-token sweet spot, or are you writing 600-token walls of text that will get split unpredictably?

Identify redundancy. Are multiple sections on the same page saying the same thing in different words? Every instance of redundancy creates a competing, lower-quality chunk.

Evaluate heading specificity. Do your headings provide enough context that a chunk prefixed with the heading hierarchy would be meaningful on its own?

Review freshness signals. Are structured data dates accurate? Is the content itself current?

What This Means for the Future of SEO

The shift from page-level to chunk-level optimization is still early. Most SEO agencies haven’t even started thinking about it. But the mechanics are clear: AI systems retrieve and cite content at the chunk level, and the quality of those chunks determines visibility.

This doesn’t replace traditional SEO. You still need crawlability, authority, technical health, and quality content. But layering chunk-aware content architecture on top of those fundamentals gives you a meaningful edge in the AI-mediated search landscape that’s rapidly becoming the norm.

The agencies and content teams that understand this shift now will be the ones whose clients show up in AI Overviews, get cited by Perplexity, and surface in ChatGPT’s browsing results. The ones that don’t will keep optimizing for a search paradigm that’s already evolving past them.