AI System Design interview questions

TL;DR: AI system design interviews test whether you can architect real, production-grade AI systems—not just explain how LLMs work. Expect questions about high-throughput LLM serving, GPU scheduling, batching, KV caching, speculative decoding, streaming protocols, safety layers, observability, and retrieval-augmented pipelines. Strong candidates demonstrate clear reasoning about latency budgets, cost trade-offs, multi-tenant isolation, privacy, red-teaming workflows, and failure modes. If you can explain how to design resilient inference services, safe tool-using agents, scalable moderation APIs, and multimodal real-time pipelines, you’ll excel in modern AI system design interviews.

The rise of large language models and real-time inference workloads has reshaped what companies expect in AI system design interviews. These interviews test how well you understand modern AI infrastructure: high-throughput serving, GPU scheduling, streaming APIs, privacy, safety, observability, retrieval-augmented generation (RAG), tool-use orchestration, and multimodal pipelines. This blog breaks down essential AI system design interview questions and teaches you how to answer them with clarity and structure.

Architecting a high-QPS LLM inference service end-to-end#

A thoughtful design answer goes beyond listing components. Interviewers want to see whether you understand operational realities: GPU scarcity, queue backlogs during traffic spikes, cost trade-offs, and how to provide consistent performance for different tenants. A good framing touches on isolation, reliability, autoscaling triggers, fallback strategies, and guardrails that keep the system resilient under unpredictable loads.

One of the most common AI system design interview questions is how to build a scalable, low-latency LLM inference service. A strong answer includes every layer, from request entry to GPU execution to safety monitoring.

1. Edge termination#

Start at the edge:

• Authentication and authorization

• Quota enforcement

• Rate limits and admission control

• Request validation

This protects backend GPU clusters from abuse and overload.

2. Routing#

After validation, route requests based on:

• Tenant (enterprise customer, free user, internal)

• Model (base, fine-tuned, domain-specific)

• Region (latency, data residency)

Routing also balances load across GPU pools.

3. GPU-serving layer#

On the serving layer, describe key optimizations:

• Dynamic batching to maximize GPU throughput

• KV cache reuse across tokens and conversation turns

• Autoscaling on signals such as queue depth or tokens/sec

• Graceful degradation: fallback to smaller models, shorter max tokens, cached responses

• Dropping optional features under pressure

4. Observability#

Expose operational metrics:

• Time to first token (TTFT)

• p95 and p99 latency

• Tokens per second

• Error and refusal rates

• GPU utilization

• Queue depth

Add per-tenant budgets to prevent noisy-neighbor effects.

Throughput versus latency in AI serving#

This trade-off is central to modern AI infrastructure. Interviewers want to hear that you understand not only the mechanics of batching but the user experience implications of each tuning choice. Strong answers reference real metrics (TTFT, p95/p99 latency) and explain how you maintain stability under high load, especially in multi-tenant environments.

Another classic topic in system design interview questions involves navigating trade-offs between tokens/sec throughput and user-perceived latency.

Trade-off#

• Large batches → higher throughput but worse TTFT

• Small batches → lower throughput but more responsive UX

Solution pattern#

Use micro-batching:

• Windows of 2–20 ms

• Early streaming of first tokens

• Backpressure when TTFT or p95 SLAs are violated

Treat TTFT and time-to-last-token as separate SLOs.

Explaining batching, KV caching, and speculative decoding#

Candidates who stand out can explain these optimizations in plain language, then connect them to GPU economics, model throughput ceilings, and customer-facing metrics. Interviewers look for how well you reason about failure modes: cache invalidation, degraded acceptance rates in speculative decoding, or situations where batching hurts latency instead of helping.

Interviewers expect deep knowledge of optimizations that power modern LLM services.

Batching#

Groups independent inference requests to increase GPU throughput and reduce cost.

KV caching#

Reuses attention key/value tensors so the model doesn’t recompute them every decode step or turn, crucial for long contexts.

Speculative decoding#

A small draft model predicts multiple tokens; the larger model verifies them. Accepted tokens reduce decode time, rejected ones fall back to standard decoding.

Together these improvements significantly lower latency and cost.

SSE vs. WebSockets for streaming AI responses#

Streaming is a critical part of user experience for LLMs and real-time assistants. Interviewers expect you to understand protocol-level behavior, proxy interactions, and how various transport choices behave at scale. Answers should also address observability and failure handling, since dropped connections or partial streams must be recoverable and traceable.

This appears frequently in AI system design interview questions, especially for chat and real-time assistants.

Choose SSE for:#

• Simple, one-way token streaming

• Proxy and CDN compatibility

• Minimal protocol overhead

Choose WebSockets for:#

• Bi-directional communication

• Tool calls

• Live cancellation or progress updates

Support heartbeats, resume tokens, and backpressure in both designs.

Prompt-injection defenses and context isolation#

AI systems are uniquely vulnerable to prompt injection because model instructions and user input occupy the same channel. Strong candidates highlight layered defenses that account for retrieval contamination, tool invocation risk, and context mixing between tenants. Interviewers appreciate when you describe measurable safety goals and continuous evaluation rather than one-off filtering.

Modern AI systems require strong safety and sandboxing.

Defenses include:#

• Immutable templated system prompts

• Sanitized retrieved context

• Constrained tool outputs (schemas)

• Whitelisted tool functions

• Structured output validation

• Safety checks before retrieval or tool execution

• Continuous red-teaming

Red-teaming and safety evaluation loops#

Modern AI launches depend on rigorous safety gates. Strong answers highlight automation, repeatability, and actionable insights—not ad hoc testing. Interviewers look for understanding of distribution shifts, adaptive adversaries, and how telemetry feeds back into the next evaluation cycle.

Companies expect continuous evaluation of model safety.

Strong answers mention:#

• Curated attack corpora and adversarial generators

• Safety scorecards gating releases

• Shadow deployments to validate behavior

• Canary rollouts with safety SLO monitoring

• Kill switch and rollback procedures

Telemetry and logging for auditability#

Telemetry isn't just debugging—it’s regulatory compliance, incident forensics, and financial accountability for AI workloads. Interviewers want to hear how you design logs for reviewability without leaking sensitive data. Strong designs include redaction, access controls, lineage, sampling strategies, and alerts tied to safety or hallucination spikes.

One of the most underestimated questions is logging.

Required logs:#

• Tenant and request IDs

• Prompt hash or redacted text

• Model name and version

• Retrieval metadata (doc IDs, scores)

• Tool invocation arguments and outputs (scrubbed)

• Safety or policy decisions

• Latency, TTFT, tokens/sec

• Cost per request

All logs require access control, retention policies, and support for incident forensics.

Designing a low-latency live-captions and diarization pipeline#

Interviewers use this question to test your multimodal reasoning and your ability to budget latency at every stage. Strong answers include fallback paths, noise robustness, jitter buffering, real-time segmentation, and how you'd operationalize a system that must remain accurate and stable even in bandwidth-constrained or privacy-sensitive environments.

This topic tests multimodal and latency-budget reasoning.

Pipeline:#

1. Audio ingestion

2. Voice Activity Detection (VAD)

3. Streaming ASR for partial and final hypotheses

4. Speaker embeddings

5. Clustering for diarization

6. Output timestamped segments

Considerations:#

• 200–300 ms latency budget

• Packet-loss resilience

• Edge or on-device inference for privacy

• Noise-robust models

Designing an AI chat assistant combining tools and RAG#

This question is about orchestration, not just retrieval. Interviewers want to understand how you enforce safety before tool execution, how you prevent hallucinated tool calls, how you track state across turns, and how you prevent context bloat. Strong answers discuss caching, memory limits, retrieval hygiene, and how the orchestrator resolves ambiguities or conflicting signals.

One of the most important questions evaluates your understanding of agent-like orchestration.

Components:#

• Orchestrator

• Hybrid retrieval (vector and lexical)

• Tool registry

• Safety and policy layer

• Memory store

Flow:#

1. Ground query with retrieval

2. Form a tool plan

3. Execute with guards

4. Stream results and citations

5. Log traces

6. Cache common answers

7. Enforce permissions

Designing a scalable AI-powered moderation API#

Moderation is a high-stakes, high-throughput workload that must balance precision, recall, safety, and cost. Interviewers expect you to discuss asynchronous escalation paths, robust retries, human-in-the-loop flows, versioned policy definitions, and caching layers that avoid reprocessing identical content. The strongest answers include monitoring strategies, appeals handling, and audit trails for disputed decisions.

Many companies evaluate candidates on safety and policy enforcement.

Architecture:#

• /classify endpoint for synchronous use

• /review endpoint for async escalation

Pipeline:#

• Fast classifiers on the hot path

• Heavy models or LLMs for borderline cases

• Human review queues for escalations

• Versioned policies

• Cached decisions for repeat content

Final thoughts#

Modern AI system design interview questions test whether you can architect AI products that are safe, scalable, observable, cost-efficient, and compliant with real deployment constraints. If you can articulate trade-offs, optimize LLM serving, enforce safety boundaries, and design robust retrieval and multimodal pipelines, you’ll stand out immediately.

Happy learning!