100x this. Garbage in = Garbage out. In similar mindset, I've seen attempts to "fix" flaky test suites by retrying failing tests 5 times until they pass. What

100x this. Garbage in = Garbage out. In similar mindset, I've seen attempts to "fix" flaky test suites by retrying failing tests 5 times until they pass. What happens: You just set a new baseline of shit allowed. This allows even more noise to enter the system and you have to rerun them even more or need increasingly more advanced tools to filter out the noise. Once the new baseline is anchored, you become dependent on the filter. Now every tool that interacts with the metrics need to be aware of the additional filter, there may be more than just the slack messages. Should your dashboard show the raw or the filtered metrics? Devils advocate: consider an alert with 99/100 false positives. The LLM may be good at classifying it as noisy but will it do a better job than a human to react to the 1 true positive? Maybe, but at the same time it allows more such noise to accumulate in the system, in effect a net negative. It's better to remove such an alert instead. Even if the numbers were turned around in favor, that's a lot of complexity added. The additional context this product provides may of course still be useful and i applaud the effort. This product space does have a lot of potential for growth and is a real pain for operators. Be careful with using it as a substitute for proper alert hygiene and culture.