Question 1

What is the universal framework for approaching AI system design interview questions?

Accepted Answer

Every AI design question can be attacked with the same repeatable method. Interviewers reward structure — walking these steps out loud, stating assumptions, and naming trade-offs beats jumping to a diagram. Use this as your spine and adapt.

The 8-step method
1. Clarify requirements & scope. Functional (what it does) + non-functional (scale/QPS, latency SLO, cost budget, quality bar, safety/compliance). Ask: who are the users, what's the volume, what latency is acceptable, what's the accuracy/hallucination tolerance?
2. Define AI-specific requirements. Required accuracy, acceptable hallucination rate, latency target (interactive vs batch), safety/regulatory constraints, and how you'll measure success (this is where AI design differs from classic system design).
3. High-level architecture. Sketch the flow: data/ingestion → model/AI core → serving/inference → application layer → feedback loop.
4. Choose the AI approach. Prompting vs RAG vs fine-tuning vs agent — and which model (frontier API vs open/self-hosted vs SLM). Justify with the requirements.
5. Deep-dive the core component. The retrieval pipeline, the agent loop, the training pipeline, or the generation pipeline — whichever

Question 2

Design ChatGPT: Training to Serving (End to End)

Accepted Answer

Two pipelines. Training: data collection & cleaning → large-scale pretraining (next-token, thousands of GPUs) → SFT on instruction/demonstration data → alignment (RLHF/RLAIF) → eval & red-teaming. Serving: API/app → context assembly (system prompt + memory + history) → moderation → inference cluster → stream tokens back.

Train: data → pretrain → SFT → RLHF/RLAIF → eval/red-team  ||  Serve: request → context assembly → safety → inference (KV cache, continuous batching, disaggregated prefill/decode) → stream

Key decisions
- Serving infra: continuous batching + PagedAttention for throughput; disaggregated prefill/decode to isolate compute-bound prefill from memory-bound decode; autoscale GPU fleet on load.
- Context management: assemble system prompt + conversation memory within the window; prompt-cache the stable prefix.
- Safety: input/output moderation classifiers; alignment baked into training.
- Feedback flywheel: thumbs up/down + logs feed future SFT/RLHF data.

INTERVIEW TRADE-OFF
The central tension is quality vs cost/latency at serving scale. Frame it as: batching maximizes throughput but adds queueing latency; larger models are better but slower/costlier; the design balanc

Question 3

Design a RAG System (Chat with Your Documents)

Accepted Answer

Let users upload docs and ask grounded, cited questions. Ingestion (offline): parse (layout-aware + OCR) → chunk → embed → index in a vector DB with metadata. Query (online): embed query → hybrid retrieve → rerank → assemble prompt with context → generate with citations.

Ingest: parse → chunk → embed → vector DB (+metadata)  ||  Query: embed → hybrid search → rerank → LLM (grounded + cited)

Key decisions
- Chunking (recursive/parent-child), hybrid search (dense + BM25), and a reranker for precision.
- Grounding + citations: answer only from context, attach sources, verify support.
- Freshness & access control via metadata (per-user filtering); incremental re-index on updates.
- Evaluation: RAGAS (faithfulness, context precision/recall) — measure retrieval and generation separately.

INTERVIEW TRADE-OFF
Retrieval quality caps the system — most effort goes into chunking, hybrid, and reranking, not the LLM. Reflex line: "separate retrieval from generation so you know which layer to fix." (See the RAG assignment for depth.)

Question 4

Design Memory for a Personal AI Assistant

Accepted Answer

Persist user context across turns and sessions. Three tiers: short-term (current context window), working (rolling summary of the session), long-term (external store split into semantic = facts/preferences, episodic = past interactions, procedural = learned routines).

Turn → extract durable facts → write to long-term store  ·  each turn: retrieve relevant memories → inject into context

Key decisions
- What to store: extract salient facts (an LLM decides what's worth remembering) vs storing everything.
- Retrieval: vector search over memories, pull only what's relevant to the current turn.
- Updating & conflict: supersede stale facts; resolve contradictions (recency/authority).
- Privacy: user-visible, editable, deletable memory (GDPR).

INTERVIEW TRADE-OFF
Storing too much pollutes context and cost; too little loses coherence. The art is selective extraction + just-in-time retrieval. This is context engineering applied to memory. (Prakhar's Rowboat/cowork work touches this directly.)

Reference in assignment: outcomeschool.com/blog/ai-agent-memory.

Question 5

Design a Conversational AI System with Memory Across Sessions

Accepted Answer

A chat system where the assistant remembers a user across days/sessions. Per-user persistent memory store + session manager; on each message, load relevant long-term memory + recent session history into context, generate, then write new durable facts back.

Key decisions
- Session vs long-term split: recent turns verbatim (short-term) + retrieved facts (long-term vector store).
- Summarization of old sessions to keep context bounded.
- Identity & scoping: memory keyed per user; multi-device sync.
- Privacy/compliance: retention policy, deletion, opt-out.

INTERVIEW TRADE-OFF
Same core as #3 — the differentiator is cross-session persistence and its privacy implications. Emphasize that memory must be user-controllable and deletable, and that you retrieve rather than dump the full history (cost + lost-in-the-middle).

Reference in assignment: outcomeschool.com/blog/ai-agent-memory.

Question 6

Design an LLM Inference Platform (vLLM-as-a-Service)

Accepted Answer

A multi-tenant service that serves many models over an OpenAI-compatible API with high throughput and low latency. Layers: API gateway (auth, rate limit, routing) → request scheduler → vLLM/SGLang worker pool (per model) → GPU fleet with autoscaling → observability.

Gateway (auth/limit/route) → scheduler → vLLM workers [PagedAttention · continuous batching · chunked prefill] → GPU autoscale

Key decisions
- Throughput: continuous (iteration-level) batching — dynamically admit/retire requests each step; fixes the ~60% idle-GPU problem of static batching.
- Memory: PagedAttention manages the KV cache like paged virtual memory, eliminating 60–80% fragmentation waste.
- Latency: chunked prefill and, at scale, disaggregated prefill/decode (separate GPU pools) to stop compute-bound prefill from stalling memory-bound decode and spiking tail latency.
- Efficiency: quantization, speculative decoding, prefix caching (RadixAttention); multi-model with autoscaling and GPU sharing.
- Serving: streaming, SLO-aware scheduling, per-tenant quotas.

INTERVIEW TRADE-OFF (SAY THE CURRENT STATE)
"By 2025–26, disaggregated prefill/decode moved from research into production at Meta, Mistral, LinkedIn (v

Question 7

Design an LLM Evaluation Platform

Accepted Answer

A platform to measure model/prompt/system quality offline and online. Components: dataset & test-case management → eval runners (batch) → metric engine (task metrics, LLM-as-judge, RAGAS, safety) → online eval (traffic sampling, A/B) → trace/observability store → dashboards + regression gating + human labeling queue.

Key decisions
- Offline + online: curated eval sets for CI/regression + sampled production traffic for real-world quality.
- Metrics by task: exact-match/F1, LLM-as-judge (with rubrics) for open-ended, RAGAS for RAG, trajectory/tool-accuracy for agents, safety classifiers.
- Regression gating: block deploys that regress the eval suite; version prompts/models.
- Tracing: span-level traces to localize failures; human-in-the-loop labeling to grow eval sets.

INTERVIEW TRADE-OFF
LLM-as-judge is scalable but imperfect (bias, inconsistency) — calibrate it against human labels and use it as a signal, not ground truth. The platform's real value is a regression suite so a model/prompt change can't silently break behavior.

Reference in assignment: outcomeschool.com/blog/llm-evaluation.

Question 8

Design a Deep Research Agent

Accepted Answer

Given a complex question, autonomously research the web and produce a cited report. Loop: planner decomposes the question into sub-questions → iterative search (web/tools) → read & extract → synthesize → reflect/verify → report with citations. Often fan out sub-agents per sub-topic in parallel.

Plan (decompose) → [search → read → extract]×N (iterative + parallel subagents) → synthesize → verify → cited report

Key decisions
- Decomposition + iterative retrieval: multi-hop via query decomposition and follow-up searches using what's learned.
- Sub-agents for parallelism & context isolation: each researches a topic in its own context, returns a summary.
- Source management & citations: track provenance, deduplicate, verify claims against sources.
- Long-running control: checkpointing, iteration/budget caps, cost/time limits.

INTERVIEW TRADE-OFF
Depth vs cost/time — deep research burns many tool calls and tokens. Bound it with iteration/budget caps and parallel subagents (context isolation keeps the orchestrator lean). This is a canonical agentic-RAG + orchestration problem — Prakhar's wheelhouse.

Question 9

Design a Multi-Agent Customer Support System

Accepted Answer

Route and resolve support tickets with specialized agents + human escalation. Flow: triage/router (intent + risk classifier) → specialized agents (billing / technical / account) each with RAG over the KB and tools (order lookup, refund, status) → guardrails on actions → escalate to human on low confidence / high risk.

Ticket → router → {billing | tech | account} agent (RAG + tools) → action guardrails → resolve or escalate to human

Key decisions
- Routing: classifier or LLM directs to the right specialist.
- Escalation logic: confidence threshold, sentiment (frustration), high-value/irreversible actions, explicit request.
- Action guardrails + HITL: refund caps require approval; least privilege on tools.
- Context handoff: preserve full context when escalating; log for QA/eval.

INTERVIEW TRADE-OFF
Autonomy vs safety — the system should resolve what it confidently can and gracefully hand off the rest. Don't over-engineer to multi-agent if a single well-toolced agent + escalation suffices; add specialization only where it earns its coordination cost.

Reference in assignment: outcomeschool.com/blog/multi-agent-systems.

Question 10

Design an AI Coding Agent

Accepted Answer

An agent that implements tasks in a real codebase. Loop: index the repo (code RAG) → plan → edit files (tools) → run tests/lint in a sandbox → read errors → iterate → open a PR. The harness (file tools, execution sandbox, verification loop, context management) is the hard part.

Repo context (code search/RAG) → plan → edit (file tools) → run tests/lint (sandbox) → fix errors → PR

Key decisions
- Codebase context: retrieval over code + dependency graph; fit large repos into context via search, not stuffing.
- Verification loop: tests/lint/type-checks as objective feedback the agent self-corrects against.
- Sandboxing: isolated execution (containers/microVMs), no prod/secret access.
- Guardrails + HITL: human reviews the PR; no destructive ops without approval.

INTERVIEW TRADE-OFF
The differentiator vs a chatbot is the execution + verification loop and codebase context, not the model. Emphasize "Agent = Model + Harness" — most failures are harness (context, tools, sandboxing). This is exactly Prakhar's Rowboat work (skill/tool-loading, code agent infra).

Question 11

Design a Code Generation and Review System

Accepted Answer

Generate code from a spec and review code for bugs/style/security. Two passes: generation (spec + repo conventions → code, grounded via RAG) → review (static analysis + LLM review for bugs/security/style + test generation) → human approval.

Key decisions
- Verification: combine deterministic static analysis / linters / tests with LLM review (LLM catches semantic issues, tools catch syntactic/security).
- Grounding: retrieve repo conventions/patterns so output fits the codebase.
- Security scanning (secrets, vuln patterns) as a hard gate.
- HITL: reviews are suggestions; a human merges.

INTERVIEW TRADE-OFF
LLM review has false positives/negatives — pair it with deterministic tools and keep a human in the loop. The value is augmenting reviewers (catching more, faster), not replacing them.

Question 12

Design an AI System for Automated Code Migration

Accepted Answer

Migrate a codebase (framework/language/version). Flow: parse source (AST + LLM understanding) → transform module-by-module (rules + LLM) → verify (compile + tests must pass) → iterate on failures → human review.

Key decisions
- Behavior preservation: tests are the contract — migrated code must pass the original test suite.
- Chunking large codebases: migrate by module/dependency order; maintain a mapping of changes.
- Hybrid rules + LLM: deterministic transforms where possible, LLM for the ambiguous parts.
- Verification loop + HITL for complex/critical modules.

INTERVIEW TRADE-OFF
Correctness is everything — a migration that "looks right" but changes behavior is worse than none. Test-driven verification and incremental, reviewable chunks are the core. Don't attempt whole-repo one-shot; migrate and verify piece by piece.

Question 13

Design a Multi-Agent Workflow System (Agents Collaborate on Complex Tasks)

Accepted Answer

A general framework where specialized agents collaborate on a multi-step task. Components: orchestrator + agent registry + shared typed state + tool layer; model the workflow as a graph/DAG of tasks with conditional edges, handoffs, parallel branches, error recovery, and human checkpoints (LangGraph-style).

Orchestrator over a state graph: nodes (agents/tools) + conditional edges + shared state + checkpoints (HITL) + recovery

Key decisions
- Orchestration: explicit state graph (durable, inspectable) beats free-form agent chatter.
- Coordination: structured handoffs, clear roles, termination conditions.
- State management + checkpointing: resume, retry, human interrupts.
- Error recovery: bounded retries, fallbacks, escalation.

INTERVIEW TRADE-OFF
Multi-agent adds power (specialization/parallelism) but also coordination cost, latency, and cascading-failure risk. Use a graph-based orchestrator for controllability, and only decompose into agents where the task genuinely needs it. (Core to Rowboat's orchestration.)

Reference in assignment: outcomeschool.com/blog/multi-agent-systems.

Question 14

Design a Text-to-Image Generation Service (Midjourney-like)

Accepted Answer

Prompt → image, at scale. Async pipeline: API → input safety filter → job queue → GPU worker pool running a diffusion model (latent diffusion) → upscale → output moderation → store (object store/CDN) → notify user.

Prompt → safety filter → queue → GPU diffusion workers → upscale → output moderation → CDN → deliver

Key decisions
- Async job queue + GPU pool: generation takes seconds — decouple request from compute, autoscale GPUs on queue depth.
- Safety (both ends): block harmful prompts (input) and NSFW/violative outputs (output moderation).
- Latency: fewer diffusion steps / distilled models for speed; caching of identical prompts.
- Cost: batch on GPUs; tiered quality/speed options.

INTERVIEW TRADE-OFF
Generation is GPU-bound and slow, so the whole design is an async queue + autoscaling GPU pool, not a synchronous API. Quality (steps/resolution) trades against latency and cost — expose it as user-selectable modes.

Question 15

Design a Music Generation Service (Suno-like)

Accepted Answer

Prompt/lyrics → song. Async pipeline: API → safety/copyright filter → queue → GPU workers (audio-token or diffusion model, possibly separate vocal + instrumental tracks) → mix/post-process → moderation → deliver.

Key decisions
- Async + GPU pool (long generation) as with images.
- Model: audio-token LM or audio diffusion; optional stem separation (vocals/instruments).
- Copyright & safety: filter prompts referencing protected artists/lyrics; watermark outputs; content moderation.
- Post-processing: mastering, format conversion.

INTERVIEW TRADE-OFF
Same async-GPU pattern as image/video, but copyright is the distinctive risk — guard against cloning specific artists and reproducing copyrighted lyrics/melodies, and consider watermarking generated audio.

Question 16

Design a Video Generation Service (Sora-like)

Accepted Answer

Prompt → video — the most compute-intensive. Async pipeline: API → safety filter → queue → large GPU cluster running a diffusion transformer over space-time → temporal consistency → progressive/preview output → moderation → CDN.

Key decisions
- Extreme compute: long, expensive jobs — robust queue, autoscaling GPU clusters, and aggressive cost management (job limits, priority tiers).
- Temporal consistency: space-time diffusion / conditioning across frames.
- Progressive delivery: low-res preview first, then full render.
- Safety: strong input + output moderation (deepfakes, harmful content), provenance watermarking (C2PA).

INTERVIEW TRADE-OFF
Cost and latency dominate — a single video can cost minutes of GPU cluster time. Design around queued async jobs, previews, priority tiers, and hard per-user limits. Safety (deepfakes) and provenance are first-class, not afterthoughts.

Question 17

Design a Multimodal Search System (Text, Image, Video)

Accepted Answer

Search across text, images, and video with any-modality queries. Ingestion: encode each item with multimodal embeddings (CLIP-style) into a shared vector space; video → sample keyframes + segment-level embeddings + transcript. Query: embed query (any modality) → ANN search → rerank → results.

Ingest: {text, image, video→keyframes+transcript} → multimodal embeddings → shared vector index  ||  Query → embed → ANN → rerank

Key decisions
- Shared embedding space so cross-modal retrieval works (text→image, image→video).
- Video handling: keyframe sampling + temporal segments + ASR transcript for text search.
- Hybrid: combine semantic embeddings with metadata/keyword (titles, tags).
- Scale: ANN + quantization for large multimedia corpora.

INTERVIEW TRADE-OFF
Video is the hard part — you can't embed a whole video as one vector, so you decompose into frames/segments/transcript, which multiplies index size. Balance granularity (recall) against storage/cost.

Question 18

Design an On-Device AI Assistant

Accepted Answer

Run an assistant locally on phone/laptop for privacy and offline use. Core: a quantized SLM (1–3B, 4-bit) via an on-device runtime (llama.cpp / CoreML / ONNX / MLX), local RAG over on-device data, and a hybrid cloud fallback for hard queries.

Key decisions
- Model size vs capability: quantized SLM that fits memory/battery; distilled for the task.
- Hybrid routing: handle common/private tasks locally, offload complex ones to the cloud (with user consent).
- Privacy-first: data stays on device; local vector store.
- Efficiency: latency, battery, thermal limits; NPU acceleration.

INTERVIEW TRADE-OFF
Capability vs on-device constraints — you sacrifice raw quality for privacy, offline, low latency, and zero per-token cost. The elegant answer is a hybrid: SLM locally, cloud escalation for the hard 10%.

Question 19

Design an AI-Powered Email Assistant

Accepted Answer

Triage, summarize, and draft email. Flow: ingest inbox → classify & prioritize (importance/intent) → summarize threads → draft replies (RAG over prior emails + context) → tools (calendar, contacts) → user approves before sending.

Key decisions
- Context: thread history + user's writing style + relevant past emails via retrieval.
- Action guardrails: never auto-send — draft + human approval; HITL for scheduling/commitments.
- Privacy: sensitive data; on-prem or strict access controls.
- Prioritization model: learn user preferences over time.

INTERVIEW TRADE-OFF
Autonomy vs trust — sending email is a semi-irreversible action, so the default is draft-and-approve, not autonomous send. Personalization (matching the user's voice) is what makes drafts usable.

Question 20

Design a Personalized Learning Assistant

Accepted Answer

An adaptive tutor. Components: a student model (knowledge state / mastery per concept) → adaptive content & question selection → RAG over curriculum → Socratic tutoring (guide, don't just answer) → progress tracking + memory of the learner.

Key decisions
- Student modeling: track mastery, misconceptions, pace (knowledge tracing).
- Adaptivity: select next content at the right difficulty (zone of proximal development).
- Pedagogy: Socratic prompting over answer-giving; hint scaffolding.
- Memory: persistent learner profile across sessions.

INTERVIEW TRADE-OFF
The AI risk is giving answers instead of teaching — design prompts/guardrails for guided discovery. The student model + adaptivity is what makes it "personalized," not just a chatbot with a textbook. (Relevant to Prakhar's Drona AI.)

AI System Design

AI System Design