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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All#model-migration2#cost14#evaluation14#reliability12#agent-skills11#architecture11#observability10#error-handling9#security9#api-design6#coding-agent6#debugging5
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#idempotency5#streaming5#structured-output5#chunking4#deployment4#distributed-systems4#rag4#system-prompt4#tool-calling4#client3#embeddings3#failure-modes3#ingestion3#mcp3#message-bus3#operations3#progressive-disclosure3#ranking3#timeline3#agent-loop2#agentic-rag2#agents-sdk2#caching2#citations2#code-review2#communication2#concurrency2#consistency2#context2#context-engineering2#context-rot2#cost-control2#data-modeling2#grounding2#hybrid-search2#langgraph2#latency2#model-routing2#multi-agent2#ordering2#pipeline-design2#prompt-basics2#prompt-engineering2#protocol2#rate-limiting2#react2#redis-streams2#responses-api2#retrieval2#retrieval-quality2#routing2#runtime2#sse2#state-management2#statelessness2#subagents2#system-design2#tool-design2#tooling2#tracing2#trade-offs2#transport2#vector-database2#versioning2#workflow2#abstention1#access-control1#agent-design1#agent-quality1#altitude1#approvals1#async1#async-task1#atomicity1#attention-budget1#auth1#av-sync1#behavioral1#bm251#candidate-selection1#capacity-planning1#chain-of-thought1#checkpointing1#ci1#citation-verification1#claude-code1#cli-design1#cloud1#compaction1#compression1#content-hash1#context-compression1#context-window1#contextual-retrieval1#cost-optimization1#cross-model1#dag1#data-quality1#database1#decision-making1#decomposition1#degradation1#deliberate-practice1#design1#diagnostics1#dimensions1#distribution1#docker1#documentation1#engineering-judgement1#engineering-tradeoffs1#eval1#event-driven1#fallback1#fan-out1#ffmpeg1#forking1#four-elements1#framework-design1#framework-selection1#golden-set1#hallucination1#handoffs1#headless1#hnsw1#hybrid1#hyde1#image-generation1#incremental-recompute1#incremental-sync1#index-maintenance1#index-routing1#indexing1#information-retrieval1#instruction-hierarchy1#intent-routing1#interrupt-merge1#interview-prep1#invalidation1#isolation1#ivfflat1#just-in-time1#knowledge-organization1#lease1#llm-as-judge1#llm-output-quality1#long-context1#loop-guard1#media-pipeline1#metadata1#metrics1#mobile1#model-selection1#multi-tenancy1#multimodal1#nodejs1#orchestration1#pagination1#parent-child1#pdf-parsing1#performance1#permissions1#persistence1#pgvector1#pipeline-reliability1#portfolio1#prioritization1#production-readiness1#prompt-assembly1#prompt-caching1#prompt-injection1#prompt-limits1#prompt-techniques1#prompt-template1#prompt-versioning1#provider-abstraction1#quality-check1#quantization1#query-transformation1#quiet-hours1#rank-fusion1#reasoning1#recall1#redis1#reflection1#refusal1#reporting1#reproducibility1#rerank1#retrieval-failure1#retrieval-metrics1#retry1#retry-semantics1#retry-strategy1#review1#rollback1#rrf1#sandbox1#sandboxing1#scalability1#scheduling1#schema-design1#scoping1#scripts1#secrets-management1#self-assessment1#self-presentation1#self-reflection1#service-architecture1#session-management1#sessions1#sharding1#skill-authoring1#skill-description1#skills1#spec1#state-machine1#stateless1#stopping-criteria1#subtitles1#task-graph1#team-governance1#testing1#tool-budget1#tool-execution1#tool-naming1#tools1#tts1#tuning1#ux1#validation1#vector-index1#verification1#workflow-engine1#xml-tags1
Prompt Engineering From Scratch in 5 Days
D5 Migrating Across Models: Differences Between Claude / GPT / Domestic Chinese Models, Organizing the System Prompt; Where to Go Next — the Claude Course or the Codex Course
When you move a prompt from one model vendor to another, where does it break most often, and how do you tell a prompt problem from a genuine capability gap?同一份提示词从一家模型迁到另一家,最常坏在哪里?你怎么区分是提示词的问题还是模型能力的问题?
Common in ChinaCommon overseasIntermediate#model-migration#cross-modelHow to reason about it · think before answering
- This screens for whether you have actually migrated a prompt. 'The other model is just worse' means no; people who have know failures cluster in four places and are rarely capability gaps.
- Four breakage points, each with a detection method: format markers — do your structural symbols leak into the output; instruction strength — does an edge case get followed literally or embellished; refusal boundaries — do adversarial cases trigger new refusals or disclaimers; length habits — compare output length and item counts on normal inputs.
- To separate prompt from capability: map failing fields to one of the four; if they match, fix the vendor block. If not, check whether failures are on normal or edge cases — capability gaps show on normal cases too, while edge-only regressions are almost always vendor-specific defaults hiding in the prompt.
- Conclusion: nine out of ten regressions are unisolated 'dialect' fixed in the vendor block; genuine capability gaps are rare and surface on normal cases.
- Follow-up: if the SDK is API-compatible, is migration free? No — compatible requests do not mean compatible interpretation, and the four breakages are easier to miss precisely because nothing crashed.
分析过程 · 先想清楚再作答
- 这题在筛「有没有真的迁过」。没迁过的人会说「换个模型效果就差了」;迁过的人知道退步几乎都落在四处,而且多数不是能力差异。
- 拆法:四处断裂各配一个识别方法。格式标签——看输出里有没有出现你用来做结构的符号;指令强度——跑边界用例看是照办还是发挥;拒答边界——跑刁难用例看有没有新的拒答或多余说明;长度习惯——对比正常输入的输出长度与条目数。
- 区分提示词问题与能力问题:先看失败用例的字段能不能对上四处之一,能对上就改厂商适配块;对不上再看失败的是正常用例还是边界用例——能力差异通常在正常用例上也会体现,而边界用例上的退步几乎都是提示词里藏着只对某一家成立的默认。
- 结论:迁移退步十有九是「方言」没隔离,改厂商适配块就能恢复;真正的能力差异少见且会在正常用例上现形。
- 可预期的追问:接口兼容(同一份 SDK 调通)是不是就不用管了?不是——接口兼容只说明请求格式一样,四处断裂照样出现,而且更容易被忽略。
Key points
- Four usual suspects: format markers, instruction strength, refusal boundaries, length habits, each with a detection method
- Map failing fields to one of the four first; a match means fix the vendor block
- Capability gaps show on normal cases; edge-only regressions are almost always prompt dialect
- API compatibility is not behavioral compatibility — rerun the test set even when no code changed
答题要点
- 四处最常坏:格式标签、指令强度、拒答边界、长度习惯,各有识别方法
- 先把失败字段对四处对号,对上就改厂商适配块
- 能力差异会在正常用例上现形;只在边界用例上退步几乎都是提示词的方言
- 接口兼容不等于行为兼容,代码没改也要跑测试集
How should a system prompt be organized so it ports across models, and how do you decide which block a given sentence belongs to?系统提示应该怎么组织才方便跨模型复用?怎么判断某一句该放哪一块?
Common in ChinaCommon overseasIntermediate#system-prompt#model-migrationHow to reason about it · think before answering
- It looks structural but tests whether you have maintained one prompt across vendors. 'Just write it clearly' means no; experienced people start with blocks.
- Three blocks, one question each. Common rules — would this sentence still hold on another vendor? Role, task, done-criteria, reasoned constraints, schema. Vendor adaptation — is this true for one vendor only? Input wrapping, example style, length hints, refusal wording, structured-output switch; one per vendor, swapped wholesale. Task variables — does this change per call? Make it a parameter.
- Two self-checks are the differentiator: delete the vendor block entirely and see if what remains is still a readable prompt; grep the common block for any vendor-specific token — tag names, API parameter names, style preferences.
- Conclusion: the payoff goes beyond migration — the common block is the longest, most stable prefix, so leading with it maximizes prompt-cache hits; vendor block fixed per vendor; variables last. It extends the D1 principle of keeping stable content in the system prompt.
- Follow-up: where do few-shot examples go? Their content is common, their rendering (wrapping tags, code-block style) is vendor-specific, so split examples into content plus rendering, or at least keep vendor tags out of them.
分析过程 · 先想清楚再作答
- 这题看似问结构,实际在考「有没有维护过多家模型共用的一份提示词」。答「写清楚一点就能通用」的人没维护过;维护过的人会先说分块。
- 拆法:三块各回答一个问题。通用规则——这一句换一家模型还成立吗,成立放这里(角色、任务、完成标准、带理由的约束、schema);厂商适配——这一句是不是只对某一家成立,是的放这里(输入包裹方式、示例风格、长度提示、拒答边界表述、结构化输出开关),每家一份整块替换;任务变量——每次调用都在变吗,是的做成参数。
- 两条自检是区分度:把厂商块整块删掉,剩下的还是不是一份能读懂的提示词;通用块里搜有没有任何一家的专属词(标签名、API 参数名、风格偏好)。
- 结论:分块的收益不只是迁移——通用块是最长最稳定的前缀,放最前面缓存命中最高;厂商块每家固定;任务变量放最后。这跟 D1 讲系统提示要放稳定内容是同一条原则的延伸。
- 追问:few-shot 示例算哪一块?示例的内容属于通用规则,示例的书写风格(包裹标签、代码块风格)属于厂商适配,所以示例最好也拆成「内容 + 渲染」两层,或者至少不带厂商专属标签。
Key points
- Three blocks: common rules that hold across vendors, a per-vendor adaptation block swapped wholesale, and per-call task variables
- Two deciding questions: does it still hold on another vendor; does it change every call
- Self-checks: the prompt stays readable with the vendor block removed; no vendor-specific tokens in the common block
- Order common, vendor, variables so the most stable prefix leads and cache hits are maximized
答题要点
- 三块:通用规则(换模型仍成立)、厂商适配(每家一份整块替换)、任务变量(每次调用的参数)
- 判据是两个问题:换一家还成立吗;每次调用都在变吗
- 自检:删掉厂商块剩下的仍可读;通用块里没有任何一家的专属词
- 顺序通用、厂商、变量,最稳定的前缀在前,缓存命中最高