Real ChatGPT answer · index seedLCQBQAlVrkocompleteB2B software · AI and automation toolsinformational
What are the main differences between using Retrieval Augmented Generation versus fine tuning when I want an AI to know my company policies
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Fan-out diagram
The searches the AI actually ran for this prompt.
SEED QUERY
What are the main differences between using Retrieval Augmented Generation versus fine tuning when I want an AI to know my company policies
1 real search · 1 real-search pass
RAG versus fine tuning official documentation retrieval augmented generation fine tuning enterprise knowledge base policiesGoogle ↗
OBSERVEDComparison
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 (1)
Classes come from the query-classifier (a local Laya model). 1 of 1 calls are low-confidence (under 50%) — hover a query in the table to see its confidence.
Searches and sources
ChatGPT1 real searches · gpt-6
RAG versus fine tuning official documentation retrieval augmented generation fine tuning enterprise knowledge base policies Google ↗
- Comparing Retrieval Augmented Generation and fine-tuning · docs.aws.amazon.com
- Retrieval augmented generation (RAG) and indexes in Microsoft Foundry - Microsoft Foundry · learn.microsoft.com
- What is RAG (Retrieval Augmented Generation)? · ibm.com
- RAG and Generative AI - Azure AI Search · learn.microsoft.com