Great question! We iterated on the prompt for several days and manually verified the results for ~100 users. The results were pretty good: https://gist.github.

Great question! We iterated on the prompt for several days and manually verified the results for ~100 users. The results were pretty good: https://gist.github.com/gaurav274/506337fa51f4df192de78d1280abea31#file-gpt4all_key_attributes-csv https://gist.github.com/gaurav274/506337fa51f4df192de78d1280... Another interesting aspect was the money spent on LLMs. We could have directly used GPT-4 to generate the "golden" table; however, it's a bit expensive — costing $60 to process the information of 1000 users. To maintain accuracy while reducing costs significantly, we set up an LLM model cascade in the EvaDB query, running GPT-3.5 before GPT-4, leading to a 11x cost reduction ($5.5). Query 1: https://github.com/pchunduri6/stargazers-reloaded/blob/228e8df017029fc34d79f4e4780bda2c2af58b12/stargazers.py#L188 https://github.com/pchunduri6/stargazers-reloaded/blob/228e8... Query 2: https://github.com/pchunduri6/stargazers-reloaded/blob/228e8df017029fc34d79f4e4780bda2c2af58b12/stargazers.py#L215 https://github.com/pchunduri6/stargazers-reloaded/blob/228e8...