Interview Bank
328 questions total; 2 shown with current filters.
CourseAllFrom Frontend Engineer to Agent Engineer in 30 DaysPrompt Engineering From Scratch in 5 DaysMastering Claude: From Conversation to Claude Code in 5 DaysMastering Codex and the OpenAI Agents SDK in 5 DaysMCP in 7 Days: Wire Tools Into Any AgentAgent Skills in 7 Days: Turn Experience Into Reusable CapabilityContext Engineering in 5 DaysRAG in 14 Days: From Retrieval to Trustworthy AnswersBuild an AI Short-Drama Production Pipeline With Agents in 14 Days
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From Frontend Engineer to Agent Engineer in 30 Days
D6 Messages, Context Engineering and Compression, Session Storage/Recovery/Forking (dg M06/M08/M09/M10)
Where do you draw the line between short-term context and long-term memory, and how do you decide where a given fact belongs?短期上下文和长期记忆的边界怎么划?一条信息该往哪放,你的判断依据是什么?
Common in ChinaCommon overseasBasic#memory#context-engineeringHow to reason about it · think before answering
- This looks conceptual but is really asking for an operational test. Reciting 'short-term lives in messages, long-term lives in a vector store' just describes the status quo and gives no signal.
- Lay out the engineering properties and the boundary draws itself: short-term context dies with the session, ships in full on every request, is billed per token and capped by the window; long-term memory spans sessions, is retrieved and injected rather than always sent, is stored per item and capped by retrieval quality.
- Give a reusable test — this is the core of the answer. Ask three questions: is it still needed after this session ends, does it expire with time, can retrieval find it again? Three yeses means long-term; a no on the first means it stays short-term. Illustrate: 'the user lives in Shanghai' is long-term, 'the user just asked me to shorten that paragraph to three sentences' is not.
- Name the common failure: stuffing all long-term memory into the prompt. Two hundred preferences accumulated over six months will both blow the window and drown the model in irrelevance. The value of long-term memory is retrieving the three or four relevant items, not the volume stored.
- Expect the follow-up on updates and expiry: memories need timestamps and provenance, and a changed preference must overwrite rather than coexist with a contradictory one. Add the deletion angle — long-term memory is the part you must be able to locate and erase when a user asks for their data to be deleted.
分析过程 · 先想清楚再作答
- 这题看着像概念题,其实考的是你有没有一条可执行的判据。背出「短期在 messages 里、长期在向量库里」只是描述现状,答不出「为什么这条该进长期」就没有区分度。
- 先把两者的工程属性摆出来,边界自然就清楚了:短期上下文随会话结束作废、全量进请求、按 token 计费、受窗口约束;长期记忆跨会话存在、不进请求而是检索后注入、按条存储、受检索质量约束。
- 给一条可复用的判据,这是本题的核心:问三句话——跨会话之后还需要吗、会随时间失效吗、能通过检索捞回来吗。三个都是「是」就进长期记忆,第一个是「否」就留在短期。举例说明:用户住上海进长期,用户刚才让我把段落改成三句话留短期。
- 点出最常见的误用:把长期记忆当上下文一次性全塞进去。用了半年攒两百条偏好,全塞进请求既撑爆窗口,又因为大量不相关记忆干扰模型判断——长期记忆的价值在于按需检索出最相关的三五条,不在于存了多少。
- 可以预期的追问:长期记忆怎么更新和失效?答要点是记忆要带时间戳和来源,用户改了主意要能覆盖旧记忆而不是并存两条矛盾的;再补一句删除权——用户要求删数据时,长期记忆是必须能定位并整体删掉的那一部分。
Key points
- Short-term context dies with the session, ships in full, and is billed per token under the window cap; long-term memory spans sessions, is retrieved on demand, and is capped by retrieval quality
- The three-question test: is it needed after this session, does it expire, can retrieval find it — three yeses means long-term
- The common failure is injecting the whole memory store, which blows the window and drowns the model in irrelevance; retrieve the three or four relevant items instead
- Long-term memories need timestamps and provenance so a changed preference overwrites the old one instead of contradicting it
- Long-term memory is the part that must be locatable and deletable per user for compliance, while short-term context simply dies with the session
答题要点
- 短期上下文随会话作废、全量进请求、按 token 计费受窗口约束;长期记忆跨会话、按需检索后注入、按条存储受检索质量约束
- 判据三问:跨会话还需要吗、会随时间失效吗、能被检索捞回来吗——三个都是就进长期记忆
- 常见误用是把长期记忆整包塞进上下文,既撑爆窗口又用不相关的记忆干扰模型,正确做法是检索最相关的三五条
- 长期记忆要带时间戳和来源,用户改主意时覆盖旧记忆,避免两条矛盾记忆并存
- 长期记忆是合规上必须能按用户定位并整体删除的那一部分,短期上下文随会话删除即可
Agent Skills in 7 Days: Turn Experience Into Reusable Capability
D1 What Skills Are: the SKILL.md Spec, Directory Layout, and Three-Stage Progressive Disclosure
What problem do Agent Skills solve, and how are they different from putting every convention into one big instruction file?Agent Skills 解决的是什么问题?它和把所有规范写进一个大的提示词文件有什么区别?
Common in ChinaCommon overseasBasic#agent-skills#context-engineeringHow to reason about it · think before answering
- The discriminator is whether you say on demand. Answering skills are reusable prompts says nothing, because that is equally true of a prompt template.
- Start with the split: tools fill a capability gap the model cannot cross on its own; skills fill an experience gap where the model can do the task but not the way your team does it.
- Then the mechanism: a persistent instruction file enters context in full every session, while a skill exposes only name and description until something matches and its body is loaded.
- Quantify the cost: twenty conventions at six thousand tokens of system prompt bill three hundred thousand tokens over a fifty-turn session, and the attention dilution costs more than the money.
- Close with the rule of thumb interviewers want: if the guidance applies every single time, it belongs in the persistent instruction file; otherwise make it a skill.
- Expected follow-up: what about prompt templates? The difference is who chooses. You pick a template; the model picks a skill by reading descriptions.
分析过程 · 先想清楚再作答
- 这题的区分度在你有没有说出「按需」两个字。只答「skill 是可复用的提示词」的人,等于没答,因为那句话对提示词模板同样成立。
- 先给分工:工具补的是能力缺口,模型本来做不到的事;技能补的是经验缺口,模型做得到但不知道你们这儿怎么做。这一刀切下去,后面的论证才站得住。
- 再给机制差异:常驻指令文件每次会话全量进上下文,skill 平时只露 name 与 description,命中才展开正文。前者的成本是固定的,后者的成本是按需的。
- 接着算代价:二十条规范写满六千 token 的系统提示,五十轮会话要重复计费三十万 token;更贵的是注意力被不相干的规则稀释,做第三件事时被第十七条干扰。
- 最后给判据,这是面试官真正想听的一句:这条经验是不是每次都用得上?是就写进常驻指令文件,不是就做成 skill。
- 可预期的追问是「那提示词模板呢」。答案是谁来挑:模板是你手动选的,skill 是模型读着 description 自己选的,触发权在模型手里。
Key points
- Tools close capability gaps, skills close experience gaps. Do not blur the two.
- A persistent instruction file costs the same tokens every turn; a skill body only enters context when it matches.
- Dumping unrelated conventions into the system prompt both costs money and dilutes attention.
- The test is whether the guidance applies every time: if yes it stays resident, if no it becomes a skill.
- Unlike a prompt template, a skill is selected by the model itself from its description.
答题要点
- 工具补能力缺口,技能补经验缺口,这是两件事,不要混着答。
- 常驻指令文件成本固定且每轮重发,skill 的正文只在命中时才进上下文。
- 把不相干的规范全塞进系统提示,除了花钱还会稀释注意力,让模型被无关规则干扰。
- 判据是「是不是每次都用得上」:是就常驻,不是就做成 skill。
- 和提示词模板的关键差别是触发权在模型手里,靠的是 description。