Confidence intervals for a GAM?

I need to estimate the proportion of Y given covariates X and Z. I could bucket the covariates and then just calculate the proportion Y for every fixed X,Z pair, but not every X,Z pair has enough datapoints for the variance to be acceptable.

Thankfully, I expect and can observe a clear relationship between Y and X,Z. Fitting a logistic gam/tensor product to the data looks "much better" but how do I quantify this "much better"?

If I fix X,Z and just count the proportion Y, I know the underlying relationship is as bernoulli distribution, I know my estimator is unbiased, and I can just read the variance formula off of wikipedia and construct confidence intervals to quantify "how correct" the predictions ought to be given my sample size.

But a tensor product/logistic gam is for sure a biased estimator (I believe some curvature exists, but the smoothing penalty has no way of knowing what curvature is real and punishes them all equivalently). Splines and 2nd derivative smooths have nice behavior but at the end of the day they're kind of arbitrary assumptions I'm making about the underlying behavior of the data that doesn't have to be true at all. How would I construct the equivalent of a confidence interval? Can I make quantifiable statements about how good the predictions coming out of my model are?

(I understand that confidence intervals don't measure how good any individual prediction or model is, but rather, measure how good the process is that we used to generate the model).