diff --git a/docs/src/faq.md b/docs/src/faq.md
index c8315464ac..72fa1f97d9 100644
--- a/docs/src/faq.md
+++ b/docs/src/faq.md
@@ -271,7 +271,7 @@ Enzyme.autodiff(Reverse, f, Active(1.2), Const(Vector{Float64}(undef, 1)), Const
 Passing in a dupliacted (e.g. differentiable) variable for `tmp` now leads to the correct answer.
 
 ```jldoctest storage
-Enzyme.autodiff(Reverse, f, Active(1.2), Duplicated(Vector{Float64}(undef, 1), Vector{Float64}(undef, 1)), Const(1), Const(5))  # Correct (returns 10.367999999999999 == 1.2^4 * 5)
+Enzyme.autodiff(Reverse, f, Active(1.2), Duplicated(Vector{Float64}(undef, 1), zeros(1)), Const(1), Const(5))  # Correct (returns 10.367999999999999 == 1.2^4 * 5)
 
 # output
 
@@ -539,4 +539,4 @@ For `d/d conj(z)`, $\frac12 \left( [u_x + i v_x] + i [u_y + i v_y] \right) = \fr
 3.1 + 2.7im
 ```
 
-Note: when writing rules for complex scalar functions, in reverse mode one needs to conjugate the differential return, and similarly the true result will be the conjugate of that value (in essence you can think of reverse-mode AD as working in the conjugate space).
\ No newline at end of file
+Note: when writing rules for complex scalar functions, in reverse mode one needs to conjugate the differential return, and similarly the true result will be the conjugate of that value (in essence you can think of reverse-mode AD as working in the conjugate space).