They're searching for multiple substrings in a single pass, regexes are the optimal solution for that.
This has buttbuttin energy. Welcome to the 80s I guess.
I've seen Claude Code went with a regex approach for a similar sentiment-related task.
I doubt you are making regex and not looking at it, even if it was AI generated.
Why? They clearly just want to log conversations that are likely to display extreme user frustration with minimal overhead. They could do a full-blown NLP-driven sentiment analysis on every prompt but I reckon it would not be as cost-effective as this.
The only time to use a regex is when searching with a human in the loop. All other uses are better handled some other way.
>They could do a full-blown NLP-driven sentiment analysis on every prompt but I reckon it would not be as cost-effective as this.
Every conversation is sent to an llm at least a thousand times the size of gpt2 which could one shot this nearly a decade ago.
Yes, but that is _what the product does_. What we are talking about is _telemetry_.
And some of the entries are too short and will create false positives. It'll match the word "offset" ("ffs"), for example. EDIT: no it won't, I missed the \b. Still sounds weird to me.
I swear this whole thread about regexes is just fake rage at something, and I bet it'd be reversed had they used something heavier (omg, look they're using an LLM call where a simple regex would have worked, lul)...