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Preview. Every figure on this site is currently a fixture, not a measurement — the live crawl has not run yet. Nothing here describes Google's actual results.

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Topic

Retrieval-augmented generation

Grounding a model in documents and live results — where search APIs and vector stores meet.

Measured 2026-W36 · 2026-08-31 · google.com, gl=us

2026-W36

The shape of this topic

Queries measured

157

every week

AI Overview coverage

55.4%

corpus average 50.8%

Organic results

8.8

average per query

Weekly churn

13%

of the top 10 changes

Google answers retrieval-augmented generation queries itself more often than it does across the corpus as a whole — 55.4 against 50.8 percent.

Citations

Who Google cites here

bm25.com 26

hyde.io 22

github.com 22

en.wikipedia.org 20

docs.graphrag.com 20

rag.com 18

docs.hyde.io 18

docs.python.org 18

graphrag.com 18

stackoverflow.com 17

developer.mozilla.org 16

aws.amazon.com 14

Rankings

Who ranks here

rag.com 75

hyde.io 66

github.com 66

bm25.com 63

geeksforgeeks.org 61

graphrag.com 60

stackoverflow.com 59

youtube.com 58

reddit.com 56

en.wikipedia.org 54

freecodecamp.org 53

medium.com 46

The gap

Cited more often than ranked

Domain	Citations	Page-one finishes	Gap
docs.python.org	18	0	+18
developer.mozilla.org	16	0	+16
aws.amazon.com	14	0	+14
pypi.org	14	0	+14

These domains earn a place in Google's own answer more often than they earn a place on page one. Whatever gets a source into an AI Overview, it is not only ranking.

Intent

By what the searcher wants

Intent	Queries	AI Overview
commercial	40	32.5%
informational	88	69.3%
comparison	22	45.5%

Intent is assigned when the query enters the corpus, not inferred afterwards, so this split is stable from week to week.

Blocks

Result features

AI Overview 55.4%

People Also Ask 56.7%

Related searches 78.3%

Knowledge panel 22.3%

Sitelinks 48.4%

Index

Queries in this topic

best retrieval-augmented generation retrieval-augmented generation comparison retrieval-augmented generation for beginners open source retrieval-augmented generation retrieval-augmented generation pricing comparison how does retrieval-augmented generation work RAG pricing RAG python is RAG free how to use RAG RAG rate limits RAG not working RAG alternatives RAG best practices RAG api RAG example what is RAG chunking strategy best practices chunking strategy python chunking strategy tutorial chunking strategy api chunking strategy example what is chunking strategy how to use chunking strategy best chunking strategy tools chunking strategy explained what is reranking reranking python reranking explained reranking example reranking api how to use reranking reranking best practices reranking tutorial best reranking tools hybrid search python hybrid search best practices best hybrid search tools hybrid search tutorial what is hybrid search hybrid search explained how to use hybrid search hybrid search api BM25 python BM25 example BM25 not working how to use BM25 BM25 review BM25 pricing BM25 alternatives is BM25 free BM25 tutorial BM25 documentation BM25 rate limits best embeddings tools how to use embeddings embeddings python embeddings example embeddings explained embeddings tutorial embeddings api embeddings best practices what is embeddings what is context window how to use context window context window python context window example best context window tools context window tutorial context window best practices context window api context window explained what is semantic search how to use semantic search best semantic search tools semantic search best practices semantic search explained semantic search tutorial semantic search python semantic search example semantic search api query expansion best practices how to use query expansion query expansion python query expansion tutorial query expansion api query expansion explained query expansion example best query expansion tools what is query expansion HyDE python HyDE alternatives HyDE example HyDE review HyDE documentation is HyDE free HyDE rate limits what is HyDE HyDE best practices HyDE pricing how to use HyDE GraphRAG documentation GraphRAG example GraphRAG not working is GraphRAG free GraphRAG alternatives GraphRAG tutorial GraphRAG python GraphRAG review how to use GraphRAG GraphRAG pricing GraphRAG api what is GraphRAG how to use contextual retrieval contextual retrieval example contextual retrieval api contextual retrieval explained contextual retrieval tutorial contextual retrieval python best contextual retrieval tools contextual retrieval best practices what is contextual retrieval how to use citation grounding citation grounding example citation grounding tutorial best citation grounding tools what is citation grounding citation grounding api citation grounding explained citation grounding python citation grounding best practices RAG vs chunking strategy RAG vs hybrid search chunking strategy vs reranking chunking strategy vs BM25 reranking vs hybrid search reranking vs embeddings hybrid search vs BM25 hybrid search vs context window BM25 vs embeddings BM25 vs semantic search embeddings vs context window embeddings vs query expansion context window vs semantic search context window vs HyDE semantic search vs query expansion semantic search vs GraphRAG query expansion vs HyDE query expansion vs contextual retrieval HyDE vs GraphRAG HyDE vs citation grounding GraphRAG vs contextual retrieval contextual retrieval vs citation grounding

API

Measure a topic of your own

Pick your queries, run them on a schedule, keep what comes back. That is the whole method — the endpoint below returns the overview, its citations and the organic results together.

curl --request GET \
  --url 'https://serpens.p.rapidapi.com/api/v1/search_ai_overview?q=best%20retrieval-augmented%20generation&gl=us&hl=en&format=json' \
  --header 'x-rapidapi-host: serpens.p.rapidapi.com' \
  --header 'x-rapidapi-key: YOUR_KEY'

Start measuring

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