diff --git a/tutorials/variational-inference/index.qmd b/tutorials/variational-inference/index.qmd
index 3ad3be5de..cd814b156 100755
--- a/tutorials/variational-inference/index.qmd
+++ b/tutorials/variational-inference/index.qmd
@@ -332,10 +332,13 @@ avg[union(sym2range[:coefficients]...)]
 
 For further convenience, we can wrap the samples into a `Chains` object to summarize the results.
 ```{julia}
-varnames = vcat(["σ²", "intercept"], ["coefficients[$i]" for i in 1:n_vars])
+varinf = Turing.DynamicPPL.VarInfo(m)
+vns_and_values = Turing.DynamicPPL.varname_and_value_leaves(Turing.DynamicPPL.values_as(varinf, OrderedDict))
+varnames = map(first, vns_and_values)
 vi_chain = Chains(reshape(z', (size(z,2), size(z,1), 1)), varnames)
 ```
 (Since we're drawing independent samples, we can simply ignore the ESS and Rhat metrics.)
+Unfortunately, extracting `varnames` is a bit verbose at the moment, but hopefully will become simpler in the near future. 
 
 Let's compare this against samples from `NUTS`: