diff --git a/src/utils.jl b/src/utils.jl
index ffdc21070..d8f9090d1 100644
--- a/src/utils.jl
+++ b/src/utils.jl
@@ -6,6 +6,67 @@ const NO_DEFAULT = NoDefault()
     @addlogprob!(ex)
 
 Add the result of the evaluation of `ex` to the joint log probability.
+
+# Examples
+
+This macro allows you to [include arbitrary terms in the likelihood](https://github.com/TuringLang/Turing.jl/issues/1332)
+
+```jldoctest; setup = :(using Distributions)
+julia> myloglikelihood(x, μ) = loglikelihood(Normal(μ, 1), x);
+
+julia> @model function demo(x)
+           μ ~ Normal()
+           @addlogprob! myloglikelihood(x, μ)
+       end;
+
+julia> x = [1.3, -2.1];
+
+julia> loglikelihood(demo(x), (μ=0.2,)) ≈ myloglikelihood(x, 0.2)
+true
+```
+
+and to [reject samples](https://github.com/TuringLang/Turing.jl/issues/1328):
+
+```jldoctest; setup = :(using Distributions, LinearAlgebra)
+julia> @model function demo(x)
+           m ~ MvNormal(zero(x), I)
+           if dot(m, x) < 0
+               @addlogprob! -Inf
+               # Exit the model evaluation early
+               return
+           end
+           x ~ MvNormal(m, I)
+           return
+       end;
+
+julia> logjoint(demo([-2.1]), (m=[0.2],)) == -Inf
+true
+```
+
+!!! note
+    The `@addlogprob!` macro increases the accumulated log probability regardless of the evaluation context,
+    i.e., regardless of whether you evaluate the log prior, the log likelihood or the log joint density.
+    If you would like to avoid this behaviour you should check the evaluation context.
+    It can be accessed with the internal variable `__context__`.
+    For instance, in the following example the log density is not accumulated when only the log prior is computed:  
+    ```jldoctest; setup = :(using Distributions)
+    julia> myloglikelihood(x, μ) = loglikelihood(Normal(μ, 1), x);
+
+    julia> @model function demo(x)
+               μ ~ Normal()
+               if DynamicPPL.leafcontext(__context__) !== PriorContext()
+                   @addlogprob! myloglikelihood(x, μ)
+               end
+           end;
+
+    julia> x = [1.3, -2.1];
+
+    julia> logprior(demo(x), (μ=0.2,)) ≈ logpdf(Normal(), 0.2)
+    true
+
+    julia> loglikelihood(demo(x), (μ=0.2,)) ≈ myloglikelihood(x, 0.2)
+    true
+    ```
 """
 macro addlogprob!(ex)
     return quote