← Start with Content Chunking

Learn how to structure content before writing so each section can be independently retrieved, scored, and cited by search engines and large language models.

Unlike content chunking, which optimizes presentation and readability, prechunking optimizes extraction and retrieval mechanics.

Prechunked content is designed to survive isolation.

Prechunking is not:

• Improving readability
• Reducing paragraph length
• Visual formatting
• Traditional UX optimization

Those are content chunking concerns. Prechunking operates at the retrieval layer, not the presentation layer.

Learn about content chunking →

Search engines and LLMs do not retrieve pages. They retrieve segments.

If a segment:

• Depends on surrounding context
• References other sections
• Uses ambiguous pronouns
• Combines multiple answers

It will not be reliably retrieved or cited. Prechunking exists to solve this failure mode.

Step 1: Define the Question Inventory First

Before writing, enumerate the exact questions the content must answer. Each question becomes one prechunk. No exceptions.

Failed example: Writing a guide about "AI SEO" without listing specific questions. Result: Content answers multiple questions per section, making retrieval ambiguous.

Step 2: Enforce Atomicity

Each prechunk must pass this test: If this section were retrieved alone, would it fully answer the question without clarification? If no, the prechunk fails.

Failed example: "This approach works better than traditional methods." Result: Requires context to identify what "this approach" refers to and what it's better than.

Step 3: Use Deterministic, Query-Shaped Headers

Headers are retrieval anchors. Use literal language like "What is prechunking in SEO" not abstract language like "Understanding prechunking".

Failed example: Header "Why This Matters" instead of "Why Prechunking Matters for AI Retrieval". Result: Header cannot be matched to specific queries.

Step 4: Constrain Prechunk Size

Ideal length: 40–120 words. Hard stop: ~150 words. Exactly one answer per prechunk.

Failed example: 300-word section answering "What is prechunking?" plus "How does it work?" plus "Why is it important?". Result: Too long, combines multiple answers, reduces citation probability.

Step 5: Remove Narrative Glue Entirely

Disallowed: "As mentioned earlier", "In conclusion", transitional filler. Each prechunk must read like a standalone answer.

Failed example: "As discussed above, prechunking requires atomicity." Result: Depends on previous context, fails isolation test.

Step 6: Citation Test (Final Gate)

Each prechunk must pass: Could this be quoted verbatim as an answer by an LLM? If not, rewrite or split.

Failed example: "This is important because it helps." Result: Too vague to quote as a definitive answer.

Ready to understand how AI systems actually retrieve and cite content? Learn about segment extraction, scoring algorithms, and citation logic.