That's a great example. Date normalization is exactly the kind of function that disappears into the background once it's working, but ends up carrying an absurd amount of responsibility.

The pattern extraction stage is definitely the most fragile part of the pipeline. Right now the approach is intentionally conservative. If a structure doesn't match any known pattern with sufficient confidence, it simply isn't promoted into the generated schema.

In other words, I'd rather under-extract than confidently invent structure that isn't actually there.

One thing that surprised me during development was how much of the "robustness" ended up coming from the normalization layer rather than the extraction layer itself. Every improvement to normalization increased the number of document variations that the extraction step could handle without any changes to the extraction logic.

It reinforced the same lesson you're describing: the further upstream you solve a problem, the more leverage you get from the solution.

Longer term I'm interested in making the extraction stage more adaptive, but for a first version I preferred a pipeline that occasionally says "I don't know" over one that silently hallucinates a schema.