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docs/src/index.md

@@ -4,6 +4,8 @@ ForwardDiffPullbacks implements pullbacks compatible with [ChainRulesCore](https
 
 This package provides the function [`fwddiff`](@ref). If wrapped around a function (i.e. `fwddiff(f)`), it will cause ChainRules (and implicitly Zygote) pullbacks to be calculated using ForwardDiff (i.e. by evaluating the original function with `ForwardDiff.Dual` numbers, possibly multiple times). The pullback will return a ChainRule thunk for each argument of the function.
 
-So `Zygote.gradient(fwddiff(f), xs...)` should yield the same result as `Zygote.gradient(f, xs...)`, but will typically be substantially faster a function that has a comparatively small number of arguments, especially if the function runs a deep calculation. Broadcasting (i.e. `g.(fwddiff(f))`) is supported as well.
+So `Zygote.gradient(fwddiff(f), xs...)` should yield the same result as `Zygote.gradient(f, xs...)`, but will typically be substantially faster for a function that has a comparatively small number of arguments, especially if the function runs a deep calculation.
+
+ForwardDiffPullbacks does come with broadcasting support, `fwddiff(f).(args...)` will use ForwardDiff to differentiate each iteration in the broadcast separately.
 
 Currently, ForwardDiffPullbacks supports functions whose arguments and result(s) are statically sized, like `Real`, `Tuple`, `StaticArrays.StaticArray` and (nested) `NamedTuple`s and plain structs. Dynamic arrays are not supported yet.