Dayward AI

Interview Bank

328 questions total; 1 shown with current filters.

Tag
235 more tags
#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-migration2#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#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

Context Engineering in 5 Days

D3 Managing Context for Tool Results and Retrieval: Loading on Demand, Summarizing and Pruning, Structured Returns

  • How do you choose between just-in-time loading and pre-inference retrieval, and how would you combine them?按需加载和预先检索怎么选?混合策略应该怎么搭?
    Common in ChinaCommon overseasIntermediate#retrieval#just-in-time#hybrid

    How to reason about it · think before answering

    1. This tests situational judgment. Calling just-in-time more advanced reads as trend-following, because pre-inference retrieval is genuinely better in many cases.
    2. Separate with three questions: can the needed material be scoped in advance (yes favors pre-retrieval), how fast does the material change (fast means indexes go stale, favoring just-in-time), and can it be fetched in one shot (multi-hop exploration forces just-in-time).
    3. Name the underlying difference: pre-retrieval hands the what-to-fetch decision to a retrieval algorithm and settles it before inference; just-in-time hands it to the model and spreads it across the run. The first is faster and more predictable, the second handles not knowing in advance.
    4. Conclusion is hybrid: preload the small, stable, always-relevant slice such as a project's standing instruction file, and use runtime search primitives for the rest. That gives a fast start without stale indexing, which is what coding agents converge on.
    5. Expect the follow-up on cost: just-in-time adds round trips and latency, and every fetched body stays in context consuming budget, so it must be paired with trimming.

    分析过程 · 先想清楚再作答

    1. 这题在考场景判断。答「按需加载更先进」的会被当成跟风,因为预先检索在很多场景里就是更好的选择,说不出它好在哪说明没做过。
    2. 怎么拆:用三个问题分开。需要的资料范围事先能不能确定(能就预先检索)、资料变化快不快(变得快索引一建就旧,偏按需加载)、一次能不能取完(要顺着线索翻好几层就只能按需)。
    3. 把两者的本质差别点出来:预先检索把「取什么」的决定权交给检索算法,在推理之前一次性做完;按需加载把这个决定权交给模型自己,在推理过程中分多次做。前者延迟低、可预测,后者能应付事先不知道要什么的情况。
    4. 结论是混合:把最稳定最常用的一小部分预先放进去(比如项目的常驻说明文件),其余靠运行时的搜索原语现取。这样既有起步速度,又不会被过期索引拖住。这也是编码类 Agent 的主流做法。
    5. 可预期的追问:按需加载的成本在哪?多了几轮往返,延迟更高,而且每一次取回的正文都会留在上下文里继续占预算——所以它必须和裁剪配套,取回来的东西该扔的时候要扔。

    Key points

    • Three questions: can scope be fixed in advance, how fast does the data change, and can it be fetched in one shot.
    • Pre-retrieval delegates the fetch decision to an algorithm before inference; just-in-time delegates it to the model during the run.
    • Most real systems are hybrid: preload the stable core, use runtime search for the rest, avoiding stale indexes.
    • Just-in-time costs round trips and latency, and fetched bodies keep consuming budget, so pair it with trimming.

    答题要点

    • 三个判断:范围能不能事先确定、资料变化快不快、一次能不能取完。
    • 预先检索把取什么的决定交给检索算法并在推理前做完;按需加载把它交给模型并分多次做。
    • 多数真实项目是混合:稳定常用的一小部分预加载,其余靠运行时搜索原语现取,避开索引过期。
    • 按需加载的代价是多轮往返与延迟,且取回的正文会继续占预算,必须和裁剪配套。