What I always wonder in these kinds of cases is: What makes you confident the AI actually did a good job since presumably you haven't looked at the thousands of client data yourself?
For all you know it made up 50% of the result.
It's the same problem factories have: they produce a lot of parts, and it's very expensive to put a full operator or more on a machine to do 100% part inspection. And the machines aren't perfect, so we can't just trust that they work.
So starting in the 1920s Walter Shewhart and Edward Deming came up with Statistical Process Control. We accept the quality of the product produced based on the variance we see of samples, and how they measure against upper and lower control limits.
Based on that, we can estimate a "good parts rate" (which later got used in ideas like Six Sigma to describe the probability of bad parts being passed).
The software industry was built on determinism, but now software engineers will need to learn the statistical methods created by engineers who have forever lived in the stochastic world of making physical products.
The main issue I had was it was surprisingly hard to get the model to consistently strip commas from dollar values, which broke the csv output I asked for. I gave up on prompt engineering it to perfection, and just looped around it with a regex check.
Otherwise, accuracy was extremely good and it surfaced a few errors in my spreadsheets over the years.
Everyone has a story of a csv formatting nightmare
It wasn't a one shot deal at all. I found the ambiguous modalities in the data and hand corrected examples to include in the prompt. After about 10 corrections and some exposition about the cases it seemed to misundestand, it got really good. Edit: not too different from a feedback loop with an intern ;)
They also remember what they did - if you spot one misunderstanding, there’s a chance they’ll be able to check all similar scenarios.
Comparing the mechanics of an LLM to human intelligence shows deep misunderstanding of one, the other, or both - if done in good faith of course.
BTW, not sure if you have experiences of delegating some works to human interns or new grads and being rewarded by disastrous results? I've done that multiple times and don't trust anyone too much. This is why we typically develop review processes, guardrails etc etc.
Oh yes I have ;)
Which is why I always explain the why behind the task.
30$ to get an view into data that would take at least x many hours of someone's time is actually super cheap, specially if the decision of that result is then to invest or not invest the x many hours to confirm it.