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This sounds very interesting, would you be able to share a little more about your process? What works and what doesn't?

Unfortunately not really, but we've found (and used in production for a year) that Claude 3.5 is perfectly capable of identifying anomalies or other points of interests in very large sets of time series data.

Think of 100-200K worth of tokens formatted like this:

The only pre-filtering we do is eliminate "obviously non relevant" data, such as series where the value is completely flat the whole time, but this was done to add more data to the context, not because Claude struggled with it (it doesn't).

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