Fill in one sentence in the prompt guard tutorial.

Author: cynikolai

recipes/responsible_ai/prompt_guard/prompt_guard_tutorial.ipynb

@@ -789,7 +789,7 @@
    "metadata": {},
    "source": [
     "\n",
-    "One good way to quickly obtain labeled training data for a use case is to use the original, non-fine tuned model itself to highlight risky examples to label, while drawing random negatives from below a score threshold. This helps address the class imbalance (attacks and risky prompts can be a very small percentage of all prompts) and includes false positive examples (which tend to be very valuable to train on) in the dataset. The use of synthetic data for specific "
+    "One good way to quickly obtain labeled training data for a use case is to use the original, non-fine tuned model itself to highlight risky examples to label, while drawing random negatives from below a score threshold. This helps address the class imbalance (attacks and risky prompts can be a very small percentage of all prompts) and includes false positive examples (which tend to be very valuable to train on) in the dataset. Generating synthetic fine-tuning data for specific use cases can also be an effective strategy."
    ]
   }
  ],