How do output filters and safety classifiers fit in the stack?
PICTURE THIS: AN LLM TURN
Simple meaning
They scan the user input and the model output for hate, self-harm, sexual content, or PII.
WHY — Tokens instead of words?
LLMs use tokens (not full words) because it helps them:
question about Safety.
trade-offs, and what you would actually do on a GenAI / LLM project - not buzzwords.
Each piece maps to a number the network can learn.
Fixed pieces are what transformers expect as input.
STEPS — What happens step by step?
Before you speak the answer, walk the interviewer through these steps:
- 1They scan the user
input and the model output for hate, self-harm, sexual content, or PII.
- 2They can block, rewrite,
or escalate.
- 3Place them after generation
and also on tool arguments that might exfiltrate data.
- 4Give an example
One tiny concrete case you can say aloud.
- 5Common mistake
What juniors usually get wrong.
- 6Close
When you pick this over the alternative.
EXAMPLE — See it in action
Here's a short line you can speak, broken into clear beats:
Note: Adapt this scaffold to your own project — keep it under 60–90 seconds.
Key takeaway
They scan the user input and the model output for hate, self-harm, sexual content, or PII. They can block, rewrite, or escalate.