Coming at this from a diffeeent angle, does anyone have any links to tutorials for use-cases? I’d love to see what vectorDB hype is about but as a regular engineer I’m unable to even grasp how to use a vectorDB
Perhaps the above comment means that the secondary factors affect the denominator - i.e more people go hiking on Saturday, so entering the Saturday lottery is worse than entering the Wednesday one
My current favorite corp speak is “we can’t wait to share it with you”. Seems like it’s in literally every product announcement. At least this one is positive
Some notes:
- based on GPT3.5 - essentially, the test was “how well can GPT produce ML code” (tune hyper parameters, base off of case studies)
- did not compare to the human case, only to other ML models (unless “human” is considered perfect, in which case GPT got 86%. Although I don’t think a human would perform at 100% of the benchmark)
Alternatively LLM’s will have gotten good enough that prompts can be general enough to not need tuned skills. I.e the average person can prompt well enough to get great results
I’ve been unable to replicate this. Could you please show me an example? I ask everyone who makes this claim and have yet to see a concrete example. I just can’t get it to do anything useful for me. I feel like I’m missing the boat!
Could you explain that “two tables” issue again? I’m trying to think of a way that DB constraints (I’m using Postgres, fwiw) couldn’t handle denormalization, but imo there’s a constraint for everything. Force a 1:1 relationship, or add a check constraint so you literally cannot denormalize.