314,552 interview questions from 6,000+ companies.
Tests prioritization under pressure across multiple projects, including trade-off judgment, stakeholder communication, and ownership of outcomes.
Tests prioritization under pressure, including trade-off judgment, stakeholder communication, and ownership of outcomes.
Explain how supervised and unsupervised learning differ, and ground the distinction in a practical ML example.
Design an eval suite to measure whether a prompt revision improves or regresses LLM performance across 1,000 representative queries.
Reduce prompt input tokens by 30% while preserving answer quality through prompt compression, measurement, and careful evaluation.
Compare how to define, evaluate, and reduce hallucinations for creative generation versus factual retrieval grounded answers.
Tests judgment in balancing model safety with user experience, plus influence, ambiguity management, and decision-making.
Protect a customer service chatbot from prompt injection and jailbreaks using layered prompting, containment, and evaluation.
Explain how to use XML tags or JSON schemas to structure LLM reasoning, outputs, and evaluation for complex tasks.
Explain how to use few-shot prompting to teach a model a precise custom conversational tone and evaluate whether it stays in character.
Design a prompt strategy that gets an LLM to handle ambiguous or nonsensical queries by asking clarifying questions instead of guessing.
Design a chatbot persona that sounds empathetic and warm while staying transparent that it is an AI assistant.
Draft onboarding and fallback prompts for a creative writing chatbot, with attention to safety, prompt quality, and evaluation.
