3 ms·
Who reads the summaries? Are they even useful to begin with? Or did this just save everyone 3-5 minutes of meaningless work?
by Shank 1y ago
Who reads the summaries? Are they even useful to begin with? Or did this just save everyone 3-5 minutes of meaningless work?
- vosper 1y agoAI reads them and identifies trends and patterns, or answers questions from PMs or others?
- cube00 1y agoAI writes inaccurate summaries and then consumes its own slop so it can hallucinate the answer to the PM's questions after misreading said slop. Much like dubbing a video tape multiple times, it's going to get worse as you add more layers text predictors.
- doorhammer 1y agoNot the op, but I did work supporting three massive call centers for an f500 ecom. It's 100% plausible it's busy work but it could also be for: - Categorizing calls into broad buckets to see which issues are trending - Sentiment analysis - Identifying surges of some novel/unique issue - Categorizing calls across vendors and doing sentiment analysis that way (looking for upticks in problem calls related to specific TSPs or whatever) - etc False positives and negatives aren't really a problem once you hit a certain scale because you're just looking for trends. If you find one, you go spot-check it and do a deeper dive to get better accuracy. Which is also how you end up with some schlepp like me listening to a few hundreds calls in a day at 8x speed (back when I was a QA data analyst) to verify the bucketing. And when I was doing it everything was based on phonetic indexing, which I can't imagine touching llms in terms of accuracy, and it still provided a ton of business value at scale.