Restructure docs

docs/src/basic_usage.md

@@ -1,7 +1,7 @@
 # Basic usage
 
 You can calculate Riemannian gradient of a function defined in its embedding in multiple ways.
-For example, `rb_onb_fd51` corresponds to a finite differencing scheme and `rb_onb_fwdd` calculates gradient using [`ForwardDiff.jl`](https://github.com/JuliaDiff/ForwardDiff.jl). [`DifferentiationInterface.jl`](https://github.com/JuliaDiff/DifferentiationInterface.jl) is used to select the backend.
+[`DifferentiationInterface.jl`](https://github.com/JuliaDiff/DifferentiationInterface.jl) can be used to select the backend.
 
 ```@example
 using ManifoldDiff
@@ -24,4 +24,6 @@ println(ManifoldDiff.gradient(s2, f, q, rb_onb_fwdd))
 println(ManifoldDiff.gradient(s2, f, q, rb_proj_zyg))
 ```
 
+In this example `rb_onb_fd51` corresponds to a finite differencing scheme, `rb_onb_fwdd` calculates gradient using [`ForwardDiff.jl`](https://github.com/JuliaDiff/ForwardDiff.jl) and `rb_proj_zyg` uses [`Zygote.jl`](https://github.com/FluxML/Zygote.jl) for reverse mode automatic differentiation.
+
 [`TangentDiffBackend`](@ref) reduces dimensionality of the problem to the intrinsic dimension of the manifold, while [`RiemannianProjectionBackend`](@ref) relies on converting Euclidean gradient in the embedding to the Riemannian one.