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This is an interesting use case for AI — I spent a lot of time at my previous company messing around with Sentry's grouping interface that was a major PITA. Would have loved to just use natural language instead of weird regex-ish matching.

Curious from y'alls testing, what type of errors do NOT seem to work well / AI has trouble tagging?

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The hardest errors to group / tag are ones that are further removed from the actual error cause (think frontend bug that causes a bad request that then causes a backend error, or an OOM because of a gradual memory leak). For these, we're looking at other ways to give more context to the LLM about an error (for example, associating frontend errors with backend ones and providing both as part of the request) but we also have limits on the LLM input size (512 tokens for the mdoel we're using, gte-large).

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