LLMs

• Practice saying the answer clearly, then explain the insurance example and the trade-off.
• A strong answer identifies the authoritative data, safe failure, and measurement—not only the framework name.

Beginner — What is an LLM good for?

• An LLM processes language using learned patterns and supplied context.
• I use it to summarize, extract, classify, explain, and propose tool actions.
• Current claim status and business permissions come from services, not the model's memory.

Intermediate — Does temperature zero guarantee the same correct result?

• No.
• It generally reduces sampling variation where supported, but it does not verify evidence or eliminate all runtime variation.
• I use schemas, authoritative data, bounded outputs, and evaluations to establish application reliability.

Senior — How do you choose a model?

• I compare candidate models on representative tasks, including failure and adversarial cases.
• I measure correctness, supported tool/schema capabilities, latency, cost per successful task, and deployment/data restrictions, then version the selected configuration and keep a rollback path.

Scenario — The model invents a claim status. What do you change?

• I remove any path that asks it to know account facts.
• An authorized tool returns canonical status; a template or model explains that result.
• I test missing claims, denied access, backend failure, and unsupported extra statements.