How can you reduce jailbreak success in a product?
PICTURE THIS: AN LLM TURN
Simple meaning
Use a clear system policy, output classifiers, and refusal evals.
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:
- 1Use a clear system
policy, output classifiers, and refusal evals.
- 2Do not let retrieved
text override system rules
- 3How it works
delimit it as untrusted data.
- 4Keep a human review
path for high-risk tools.
- 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
Use a clear system policy, output classifiers, and refusal evals. Do not let retrieved text override system rules