add note about need for hyperoptimization in abstractgps surrogate

docs/src/abstractgps.md

@@ -2,7 +2,10 @@
 
 Gaussian Process regression in Surrogates.jl is implemented as a simple wrapper around the [AbstractGPs.jl](https://github.com/JuliaGaussianProcesses/AbstractGPs.jl) package. AbstractGPs comes with a variety of covariance functions (kernels). See [KernelFunctions.jl](https://github.com/JuliaGaussianProcesses/KernelFunctions.jl/) for examples.
 
-
+!!! note
+    The examples below demonstrate the use of AbstractGPs with out-of-the-box settings without hyperparameter optimization (i.e. without changing parameters like lengthscale, signal variance and noise variance.) Beyond hyperparameter optimization, careful initialization of hyperparameters and priors on the parameters is required for this surrogate to work properly. For more details on how to fit GPs in practice, check out [A Practical Guide to Gaussian Processes](https://infallible-thompson-49de36.netlify.app/).
+
+    Also see this [example](https://juliagaussianprocesses.github.io/AbstractGPs.jl/stable/examples/1-mauna-loa/#Hyperparameter-Optimization) to understand hyperparameter optimization with AbstractGPs.
 ## 1D Example 
 In the example below, the 'gp_surrogate' assignment code can be commented / uncommented to see how the different kernels influence the predictions.