diff --git a/Project.toml b/Project.toml
index 06cba77ab..7ee7d2f97 100644
--- a/Project.toml
+++ b/Project.toml
@@ -1,6 +1,6 @@
 name = "DynamicPPL"
 uuid = "366bfd00-2699-11ea-058f-f148b4cae6d8"
-version = "0.28.1"
+version = "0.28.2"
 
 [deps]
 ADTypes = "47edcb42-4c32-4615-8424-f2b9edc5f35b"
diff --git a/src/logdensityfunction.jl b/src/logdensityfunction.jl
index 8935edc12..9e86590fa 100644
--- a/src/logdensityfunction.jl
+++ b/src/logdensityfunction.jl
@@ -76,6 +76,46 @@ function getcontext(f::LogDensityFunction)
     return f.context === nothing ? leafcontext(f.model.context) : f.context
 end
 
+"""
+    getmodel(f)
+
+Return the `DynamicPPL.Model` wrapped in the given log-density function `f`.
+"""
+getmodel(f::LogDensityProblemsAD.ADGradientWrapper) =
+    getmodel(LogDensityProblemsAD.parent(f))
+getmodel(f::DynamicPPL.LogDensityFunction) = f.model
+
+"""
+    setmodel(f, model[, adtype])
+
+Set the `DynamicPPL.Model` in the given log-density function `f` to `model`.
+
+!!! warning
+    Note that if `f` is a `LogDensityProblemsAD.ADGradientWrapper` wrapping a
+    `DynamicPPL.LogDensityFunction`, performing an update of the `model` in `f`
+    might require recompilation of the gradient tape, depending on the AD backend.
+"""
+function setmodel(
+    f::LogDensityProblemsAD.ADGradientWrapper,
+    model::DynamicPPL.Model,
+    adtype::ADTypes.AbstractADType,
+)
+    # TODO: Should we handle `SciMLBase.NoAD`?
+    # For an `ADGradientWrapper` we do the following:
+    # 1. Update the `Model` in the underlying `LogDensityFunction`.
+    # 2. Re-construct the `ADGradientWrapper` using `ADgradient` using the provided `adtype`
+    #    to ensure that the recompilation of gradient tapes, etc. also occur. For example,
+    #    ReverseDiff.jl in compiled mode will cache the compiled tape, which means that just
+    #    replacing the corresponding field with the new model won't be sufficient to obtain
+    #    the correct gradients.
+    return LogDensityProblemsAD.ADgradient(
+        adtype, setmodel(LogDensityProblemsAD.parent(f), model)
+    )
+end
+function setmodel(f::DynamicPPL.LogDensityFunction, model::DynamicPPL.Model)
+    return Accessors.@set f.model = model
+end
+
 # HACK: heavy usage of `AbstractSampler` for, well, _everything_, is being phased out. In the mean time
 # we need to define these annoying methods to ensure that we stay compatible with everything.
 getsampler(f::LogDensityFunction) = getsampler(getcontext(f))
diff --git a/test/logdensityfunction.jl b/test/logdensityfunction.jl
index ea70ace29..beda767e6 100644
--- a/test/logdensityfunction.jl
+++ b/test/logdensityfunction.jl
@@ -1,4 +1,24 @@
-using Test, DynamicPPL, LogDensityProblems
+using Test, DynamicPPL, ADTypes, LogDensityProblems, LogDensityProblemsAD, ReverseDiff
+
+@testset "`getmodel` and `setmodel`" begin
+    @testset "$(nameof(model))" for model in DynamicPPL.TestUtils.DEMO_MODELS
+        model = DynamicPPL.TestUtils.DEMO_MODELS[1]
+        ℓ = DynamicPPL.LogDensityFunction(model)
+        @test DynamicPPL.getmodel(ℓ) == model
+        @test DynamicPPL.setmodel(ℓ, model).model == model
+
+        # ReverseDiff related
+        ∇ℓ = LogDensityProblemsAD.ADgradient(:ReverseDiff, ℓ; compile=Val(false))
+        @test DynamicPPL.getmodel(∇ℓ) == model
+        @test DynamicPPL.getmodel(DynamicPPL.setmodel(∇ℓ, model, AutoReverseDiff())) ==
+            model
+        ∇ℓ = LogDensityProblemsAD.ADgradient(:ReverseDiff, ℓ; compile=Val(true))
+        new_∇ℓ = DynamicPPL.setmodel(∇ℓ, model, AutoReverseDiff())
+        @test DynamicPPL.getmodel(new_∇ℓ) == model
+        # HACK(sunxd): rely on internal implementation detail, i.e., naming of `compiledtape`
+        @test new_∇ℓ.compiledtape != ∇ℓ.compiledtape
+    end
+end
 
 @testset "LogDensityFunction" begin
     @testset "$(nameof(model))" for model in DynamicPPL.TestUtils.DEMO_MODELS
diff --git a/test/turing/Project.toml b/test/turing/Project.toml
index 501359253..ed2b08ce5 100644
--- a/test/turing/Project.toml
+++ b/test/turing/Project.toml
@@ -1,6 +1,7 @@
 [deps]
 Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
 DynamicPPL = "366bfd00-2699-11ea-058f-f148b4cae6d8"
+HypothesisTests = "09f84164-cd44-5f33-b23f-e6b0d136a0d5"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 MCMCChains = "c7f686f2-ff18-58e9-bc7b-31028e88f75d"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"