3 ms·
Most of the stuff it’s actually good at (like NLP tasks) are both super boring and require a secondary layer of processing to catch hallucinations. Not as coo
by bugglebeetle 3y ago
Most of the stuff it’s actually good at (like NLP tasks) are both super boring and require a secondary layer of processing to catch hallucinations. Not as cool of a sales pitch to everyone on the “it’s alive!” hype train.
- msp26 3y agoAny advice for people building the second layer stuff?
- bugglebeetle 3y agoJust basic sanity checking. You can use an LLM call for this or something procedural. Generally, it’s just of the order of like “does the output conform to this structure”, “is the returned text actually in the input” etc
- fhd2 3y agoI've been working on NLP stuff using LLMs for a while, and it's not that the problems aren't somewhat interesting, but solving them is ridiculously tedious. Most of the time on that project I've just played around with different prompts to make it do what I'm hoping for, with almost no mental process for understanding problems and solutions, mostly just randomly coming up with experiments and reading the results very carefully, checking how consistent they are. I moved towards finding and isolating the parts LLMs are good (and reliable!) at and using deterministic approaches for everything I possibly can. That part is not too tedious, but all this black box trial and error (with all the waiting and errors)... Luckily the client doesn't expect LLMs to be what the hype says and mainly just wants some reasonably useful features so they can say they use AI - wouldn't enjoy dealing with someone thinking it's super easy and I just need to write a few scripts because they tried this in ChatGPT once.