Nice writeup! Here is another example of using LLMs is to augmenting existing software that we are exploring -- specifically SQL database systems. We are using

Nice writeup! Here is another example of using LLMs is to augmenting existing software that we are exploring -- specifically SQL database systems. We are using LLMs inside a SQL query to power a "soft join" between SQL tables for when a correspondence is only implied (e.g. different address formats, etc.). --- Create a reference table that maps neighborhoods to zipcodes using ChatGPT CREATE TABLE reference_table AS SELECT parkname, parktype, ChatGPT( "Return the San Francisco neighborhood name when provided with a zipcode. The possible neighborhoods are: {neighbourhoods_str}. The response should an item from the provided list. Do not add any more words.", zipcode) FROM postgres_db.recreational_park_dataset; --- Map Airbnb listings to park SELECT airbnb_listing.neighbourhood FROM postgres_db.airbnb_listing JOIN reference_table ON airbnb_listing.neighbourhood = reference_table.response; More details on LLM-powered joins and EvaDB: https://medium.com/evadb-blog/augmenting-postgresql-with-ai-using-evadb-b3db294adf35 https://medium.com/evadb-blog/augmenting-postgresql-with-ai-..., https://github.com/georgia-tech-db/evadb https://github.com/georgia-tech-db/evadb