diff --git a/tutorials/docs-16-using-turing-external-samplers/external-samplers.jmd b/tutorials/docs-16-using-turing-external-samplers/external-samplers.jmd
index 858798adf..e6b9a2b13 100644
--- a/tutorials/docs-16-using-turing-external-samplers/external-samplers.jmd
+++ b/tutorials/docs-16-using-turing-external-samplers/external-samplers.jmd
@@ -151,19 +151,19 @@ There are several characteristics to note in these functions:
   - The functions must follow the displayed signatures.
   - The output of the functions must be a transition, the current state of the sampler, and a sample, what is saved to the MCMC chain.
 
-The last requirement is that the transition must be structured with a field `θ` which contains the values of the parameters of the model for said transition.
+The last requirement is that the transition must be structured with a field `θ`, which contains the values of the parameters of the model for said transition.
 This allows `Turing` to seamlessly extract the parameter values at each step of the chain when bundling the chains.
-Note that if the external sampler produces transitions that Turing cannot parse the bundling of the samples will be different or fail.
+Note that if the external sampler produces transitions that Turing cannot parse, the bundling of the samples will be different or fail.
 
 For practical examples of how to adapt a sampling library to the `AbstractMCMC` interface, the readers can consult the following libraries:
 
-  - <a href="https://github.com/TuringLang/AdvancedMH.jl/blob/458a602ac32a8514a117d4c671396a9ba8acbdab/src/mh-core.jl#L73-L115">AdvancedMH</a>
-  - <a href="https://github.com/TuringLang/AdvancedHMC.jl/blob/762e55f894d142495a41a6eba0eed9201da0a600/src/abstractmcmc.jl#L102-L170">AdvancedHMC</a>
-  - <a href="https://github.com/JaimeRZP/MicroCanonicalHMC.jl/blob/master/src/abstractmcmc.jl">MicroCanonicalHMC</a>
+  - [AdvancedMH](https://github.com/TuringLang/AdvancedMH.jl/blob/458a602ac32a8514a117d4c671396a9ba8acbdab/src/mh-core.jl#L73-L115)
+  - [AdvancedHMC](https://github.com/TuringLang/AdvancedHMC.jl/blob/762e55f894d142495a41a6eba0eed9201da0a600/src/abstractmcmc.jl#L102-L170)
+  - [MicroCanonicalHMC](https://github.com/JaimeRZP/MicroCanonicalHMC.jl/blob/master/src/abstractmcmc.jl)
 
 # Refences
 
-[^1]: Xu et al, <a href="http://proceedings.mlr.press/v118/xu20a/xu20a.pdf">AdvancedHMC.jl: A robust, modular and efficient implementation of advanced HMC algorithms</a>, 2019
-[^2]: Zhang et al, <a href="https://arxiv.org/abs/2108.03782">Pathfinder: Parallel quasi-Newton variational inference</a> , 2021
-[^3]: Robnik et al, <a href="https://arxiv.org/abs/2212.08549">Microcanonical Hamiltonian Monte Carlo</a>, 2022
-[^4]: Robnik and Seljak, <a href="https://arxiv.org/abs/2303.18221">Langevine Hamiltonian Monte Carlo</a>, 2023
+[^1]: Xu et al., [AdvancedHMC.jl: A robust, modular and efficient implementation of advanced HMC algorithms](http://proceedings.mlr.press/v118/xu20a/xu20a.pdf), 2019
+[^2]: Zhang et al., [Pathfinder: Parallel quasi-Newton variational inference](https://arxiv.org/abs/2108.03782), 2021
+[^3]: Robnik et al, [Microcanonical Hamiltonian Monte Carlo](https://arxiv.org/abs/2212.08549), 2022
+[^4]: Robnik and Seljak, [Langevine Hamiltonian Monte Carlo](https://arxiv.org/abs/2303.18221), 2023