JuliaTrustworthyAI
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@@ -207,7 +207,7 @@ prediction_set(mach, refined_query)[2]
 
 Below we include a short demo video that shows the REPL-based chatbot in action.
 
-![](../../../docs/src/www/demo_llm.gif)
+![](../www/demo_llm.gif)
 
 ## Final Remarks
 
 
@@ -188,7 +188,7 @@ prediction_set(mach, refined_query)[2]
 
 Below we include a short demo video that shows the REPL-based chatbot in action.
 
-![](/docs/src/www/demo_llm.gif)
+![](../www/demo_llm.gif)
 
 ## Final Remarks
 
 
@@ -164,7 +164,7 @@ Well, for low coverage rates (roughly ``<0.9``) the conformal prediction set doe
 These two examples illustrate an interesting point: for regions characterized by high predictive uncertainty, conformal prediction sets are typically empty (for low coverage) or large (for high coverage). While set-valued predictions may be something to get used to, this notion is overall intuitive. 
 
 ```{julia}
-#| output: true
+#| output: false
 #| label: fig-anim
 #| fig-cap: "The effect of the coverage rate on the conformal prediction set. Softmax probabilities are shown on the left. The size of the prediction set is shown on the right."