diff --git a/usage/external-samplers/index.qmd b/usage/external-samplers/index.qmd
index 41ca459e0..4d5c839ca 100755
--- a/usage/external-samplers/index.qmd
+++ b/usage/external-samplers/index.qmd
@@ -130,11 +130,11 @@ The output of both of these methods must be a tuple containing:
  - a 'transition', which is essentially the 'visible output' of the sampler: this object is later used to construct an `MCMCChains.Chains`;
  - a 'state', representing the current state of the sampler, which is passed to the next step of the MCMC algorithm.
 
-Apart from this, your sampler state should also implement `Turing.Inference.getparams(model, transition)` to return the parameters of the model as a vector.
+Apart from this, your sampler state should also implement `AbstractMCMC.getparams(model, state)` to return the parameters of the model as a vector.
 Here, `transition` represents the first output of the `step` function.
 
 ```julia
-function Turing.Inference.getparams(model::DynamicPPL.Model, state::MyTransition)
+function AbstractMCMC.getparams(model::AbstractMCMC.LogDensityModel, state::MyState)
     # Return a vector containing the parameters of the model.
 end
 ```