diff --git a/docs/examples/batch_classification.md b/docs/examples/batch_classification.md
index c6980c4..5c0d02d 100644
--- a/docs/examples/batch_classification.md
+++ b/docs/examples/batch_classification.md
@@ -219,3 +219,39 @@ Since everything is already annotated with Pydantic, this code is very simple to
 !!! warning "Where do tags come from?"
 
     I just want to call out that here you can also imagine the tag spec IDs and names and instructions for example could come from a database or somewhere else. I'll leave this as an exercise to the reader, but I hope this gives us a clear understanding of how we can do something like user-defined classification.
+
+## Improving the Model
+
+There's a couple things we could do to make this system a little bit more robust.
+
+1. Use confidence score:
+
+```python
+class TagWithConfidence(Tag):
+    confidence: float = Field(..., ge=0, le=1, description="The confidence of the prediction, 0 is low, 1 is high")
+```
+
+2. Use multiclass classification:
+
+Notice in the example we use Iterable[Tag] vs Tag. This is because we might want to use a multiclass classification model that returns multiple tag!
+
+```python
+await client.chat.completions.create(
+    model="gpt-4-turbo-preview",
+    messages=[
+        {
+            "role": "system",
+            "content": "You are a world-class text tagging system.",
+        },
+        {
+            "role": "user",
+            "content": f"Describe the following text: `{text}`"},
+        {
+            "role": "user",
+            "content": f"Here are the allowed tags: {allowed_tags_str}",
+        },
+    ],
+    response_model=Iterable[Tag],
+    validation_context={"tags": tags},
+)
+```