AI 时代有个挺荒诞的现象: 同时订阅 ChatGPT、Claude、Cursor,天天研究哪个模型最强,结果工作时还是在几个窗口之间来回复制粘贴。 每个月花的钱越来越多,节省的时间却没多少。 其实判断一个 AI 工具值不值得续费,可以只看三件事: 帮你省了多少时间?结果需要返工多少次?完成一次任务要花多少钱? 工具可以不断换,但别把研究工具本身变成了新的工作。
Got an AI engineer interview coming up? These 25 questions come up again and again. Prep them and you'll walk in ready: • What's the difference between a chatbot and an agent? • How does an agent decide which tool to use? • Explain the reasoning loop: think, act, observe, repeat • When would you use RAG instead of fine-tuning? • How do embeddings and vector databases work together? • How do you handle a context window that's too small? • Short-term vs long-term memory: how would you design both? • What is MCP and why does it matter? • Single agent or multi-agent: when do you pick each? • How does an orchestrator split work between sub-agents? • How do you stop an agent from looping forever? • What guardrails would you add before giving it real tools? • Where should a human approve actions? • How do you evaluate whether an agent actually works? • What metrics would you track in production? • How do you debug an agent that gave a wrong answer? • How do you reduce hallucinations? • How do you cut token cost without hurting quality? • How do you reduce latency in a multi-step agent? • How would you handle a tool call that fails? • How do you protect against prompt injection? • How would you design an agent that books flights? • How would you design a customer support agent? • What breaks first when you scale agents to 1M users? • Tell me about an agent you built and what failed Most candidates can explain an LLM. Very few can design a system around one. Bookmark this and send it to someone with an interview this month.