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How do I use vector embeddings and semantic search principles to restructure our technical documentation for better AI crawler comprehension?

ChatGPTgpt-62 queries11/1/2026
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Fan-out diagram

The searches the AI actually ran for this prompt.

SEED QUERY
How do I use vector embeddings and semantic search principles to restructure our technical documentation for better AI crawler comprehension?
2 real searches · 1 real-search pass
technical documentation semantic search vector embeddings chunking metadata retrieval best practices official documentationGoogle ↗
OBSERVEDOther / navigational
Google Search Central documentation AI search crawling structured content headings internal links helpful contentGoogle ↗
OBSERVEDOther / navigational

Badge and edge weight = share of passes that produced the query. Hover a query for its intent and routing format.

Query classes

Google's semantic query class for every sub-query: what kind of answer each search is after, simulated vs real.

ChatGPT real searches (2)
Short factOther / navigational100%ComparisonConsequenceReasonDefinitionInstructionYes / no

Classes come from the query-classifier (a local Laya model). 2 of 2 calls are low-confidence (under 50%) — hover a query in the table to see its confidence.

Searches and sources

ChatGPT2 real searches · gpt-6
technical documentation semantic search vector embeddings chunking metadata retrieval best practices official documentation Google ↗
Google Search Central documentation AI search crawling structured content headings internal links helpful content Google ↗

Queries

2 queries · click a column to sort
Query / typeSearchIntentQuery classRouting formatClusterFreq ↓Weight
technical documentation semantic search vector embeddings chunking metadata retrieval best practices official documentation
OBSERVED
Google ↗
AI Mode ↗
—Other / navigational——1x
of 1
Google Search Central documentation AI search crawling structured content headings internal links helpful content
OBSERVED
Google ↗
AI Mode ↗
—Other / navigational——1x
of 1