How do you A/B test prompts in production?
PICTURE THIS: DATA SPLIT
Fit on train, tune on val, report on test once.
Simple meaning
Randomize at the session, log prompt version, and measure a north-star such as task success plus cost and latency.
WHY — Tokens instead of words?
LLMs use tokens (not full words) because it helps them:
question about Evals.
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:
- 1Randomize at the session,
log prompt version, and measure a north-star such as task success plus cost and latency.
- 2Why it exists
Guardrail metrics must not regress.
- 3Stop early if safety
or faithfulness drops even if click-through rises.
- 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
Randomize at the session, log prompt version, and measure a north-star such as task success plus cost and latency. Guardrail metrics must not regress.