diff --git a/docs_src/streaming/streaming_inference.py b/docs_src/streaming/streaming_inference.py
new file mode 100644
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+++ b/docs_src/streaming/streaming_inference.py
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+"""
+Streaming inference example using StreamingResponse.
+
+This pattern is useful for long-running workloads such as machine learning
+or large language model inference, where returning partial results improves
+latency and user experience.
+"""
+
+import time
+from typing import Generator
+
+from fastapi import FastAPI
+from fastapi.responses import StreamingResponse
+
+app = FastAPI()
+
+
+def fake_model_inference(prompt: str) -> Generator[str, None, None]:
+    """
+    Simulates token-by-token inference.
+
+    In a real application, this could wrap a machine learning model that
+    yields partial outputs as they are generated.
+    """
+    try:
+        for i in range(10):
+            # Simulate computation time (e.g. model forward pass)
+            time.sleep(0.2)
+            yield f"token_{i} for prompt='{prompt}'\n"
+    except GeneratorExit:
+        # This is triggered when the client disconnects early.
+        # Cleanup logic for model inference can be placed here.
+        print("Client disconnected, stopping inference")
+
+
+@app.get("/stream")
+def stream(prompt: str) -> StreamingResponse:
+    """
+    Stream inference results incrementally to the client.
+
+    This endpoint returns partial results as they become available instead
+    of waiting for the full inference to complete, making the user experience better.
+    """
+    return StreamingResponse(
+        fake_model_inference(prompt),
+        media_type="text/plain",
+    )