:saluting_face we debated whether to do or not to do it like this. We were scared of people scraping us (among other things) and decided to be bad

Thanks for trying it out!

Overall, we improve any AI answer - we've been integrated into AI search experiences (the most common/obvious use case), content generation use cases (eg https://capitol.ai/), but we're excited to see what else people come up with!

It does (and we're working on making it better)! Check out our docs here -> https://docs.trytako.com/documentation/integrations-and-exam...

The description seems to invert RAG: RAG (simplified a bit) conducts a search, presents the results to an LLM, and then the LLM produces output based on the search. In GAS as described the LLM analyzes the input query to generate the search query, then a search is run, and the results of the search are presented to the user. With GAS, you always get specific authoritative (to the extent that describes the universe searched) documents, any hallucination will impact document selection not correctness of info. Wtih RAG you get an LLM analysis of potentially many authoritative (with the same caveats as before) documents, but hallucination can affect the accuracy of the presented response as well as its relevance.