Real ChatGPT answer · index seedBNmgRFC2eP0completeB2B software · AI and automation toolsinformational
Can you explain how retrieval-augmented generation works and whether we need a dedicated vector database for our internal knowledge base search?
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The searches the AI actually ran for this prompt.
SEED QUERY
Can you explain how retrieval-augmented generation works and whether we need a dedicated vector database for our internal knowledge base search?
1 real search · 1 real-search pass
RAG retrieval augmented generation architecture vector database when needed hybrid search official documentationGoogle ↗
OBSERVED
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.
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Searches and sources
ChatGPT1 real searches · gpt-6
RAG retrieval augmented generation architecture vector database when needed hybrid search official documentation Google ↗
- What is retrieval-augmented generation, and what does it do for generative AI? · github.blog
- Design and Develop a RAG Solution on Azure - Azure Architecture Center · learn.microsoft.com
- RAG and Generative AI - Azure AI Search · learn.microsoft.com
- RAG · elastic.co