How do I approach ML System Design interview questions?

ML System Design questions require understanding of core concepts and practice. PracHub provides solutions with explanations to help you master ml system design interviews.

What difficulty level is this interview question?

This is a hard difficulty ML System Design question, commonly asked during Onsite rounds at Anthropic.

What role is this question designed for?

This question is commonly asked for Software Engineer candidates at Anthropic during technical interviews.

Design a prompt processing backend | Anthropic Interview Question

Quick Overview

Design a prompt processing backend evaluates ML product requirements, data/labeling, modeling, serving architecture, evaluation, monitoring, and trade-offs in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

Design a prompt processing backend

System Design: Background Processing Backend for LLM Prompts

Context

Design a multi-tenant backend that processes large language model (LLM) prompts asynchronously. Clients submit prompts via an API and later poll for status/results or receive callbacks via webhooks. The system must support reliability, scale, and cost controls.

Requirements

APIs
- Submit prompts (with idempotency keys), poll job status, fetch results, register webhooks/callbacks.
Job orchestration
- Queueing, prioritization (e.g., realtime vs bulk), worker pools, retries, dead-letter queues (DLQ).
Model routing
- Route requests to appropriate model/provider based on policy (latency/cost/quality/capacity).
Prompt versioning
- Manage template versions and the exact prompt/model context used for reproducibility.
Idempotency
- Ensure duplicate submissions do not create duplicate work/charges.
Retries and DLQ
- Automatic retry with backoff; poison message handling.
Result storage
- Store inputs/outputs/metadata, enable polling and callback delivery; set retention policies.
Observability
- Metrics, logs, traces; per-tenant dashboards, alerting, audits.
Non-functionals
- Scaling and capacity planning, cost control, rate limiting, PII/security, and SLAs/SLOs.
Follow-up

Support streaming partial outputs and cancellation of in-flight jobs.

Describe the architecture, data flows, and key design choices. Provide concrete API designs and operational policies.

Constraints & Assumptions

Preserve the scope, facts, inputs, and requested outputs from the prompt above.
If the prompt leaves a detail unspecified, state a reasonable assumption before relying on it.
Keep the answer interview-ready: concise enough to present, but concrete enough to implement or evaluate.

Clarifying Questions to Ask

Clarify users, core use cases, read/write patterns, scale, latency, availability, and data retention.
State explicit assumptions before making sizing or architecture decisions.
Prioritize the functional path first, then address reliability, security, observability, and rollout.

What a Strong Answer Covers

A scoped requirements summary with concrete non-goals and success metrics.
ML-specific data, model, evaluation, serving, and monitoring choices.
Reasoned trade-offs among simple and scalable designs, including bottlenecks and failure modes.
A validation, monitoring, migration, and launch plan appropriate for the risk level.

Follow-up Questions

What breaks first at 10x traffic or data volume?
How would you degrade gracefully during dependency failures?
What metrics and alerts would prove the design is healthy after launch?

Quick Overview

Requirements

APIs

Submit prompts (with idempotency keys), poll job status, fetch results, register webhooks/callbacks.

Job orchestration

Queueing, prioritization (e.g., realtime vs bulk), worker pools, retries, dead-letter queues (DLQ).

Model routing

Route requests to appropriate model/provider based on policy (latency/cost/quality/capacity).

Prompt versioning

Manage template versions and the exact prompt/model context used for reproducibility.

Idempotency

Ensure duplicate submissions do not create duplicate work/charges.

Retries and DLQ

Automatic retry with backoff; poison message handling.

Result storage

Store inputs/outputs/metadata, enable polling and callback delivery; set retention policies.

Observability

Metrics, logs, traces; per-tenant dashboards, alerting, audits.

Non-functionals

Scaling and capacity planning, cost control, rate limiting, PII/security, and SLAs/SLOs.

Follow-up

Support streaming partial outputs and cancellation of in-flight jobs.

Describe the architecture, data flows, and key design choices. Provide concrete API designs and operational policies.

Constraints & Assumptions

Preserve the scope, facts, inputs, and requested outputs from the prompt above.

If the prompt leaves a detail unspecified, state a reasonable assumption before relying on it.

Keep the answer interview-ready: concise enough to present, but concrete enough to implement or evaluate.

Clarifying Questions to Ask

Clarify users, core use cases, read/write patterns, scale, latency, availability, and data retention.

State explicit assumptions before making sizing or architecture decisions.

Prioritize the functional path first, then address reliability, security, observability, and rollout.

What a Strong Answer Covers

A scoped requirements summary with concrete non-goals and success metrics.

ML-specific data, model, evaluation, serving, and monitoring choices.

Reasoned trade-offs among simple and scalable designs, including bottlenecks and failure modes.

A validation, monitoring, migration, and launch plan appropriate for the risk level.

Follow-up Questions

What breaks first at 10x traffic or data volume?

How would you degrade gracefully during dependency failures?

What metrics and alerts would prove the design is healthy after launch?

Design a prompt processing backend

Quick Overview

Design a prompt processing backend

Design a prompt processing backend

System Design: Background Processing Backend for LLM Prompts

Context

Requirements

Constraints & Assumptions

Clarifying Questions to Ask

What a Strong Answer Covers

Follow-up Questions

Submit Your Answer to Earn 20XP

Design a prompt processing backend

Quick Overview

Design a prompt processing backend

Design a prompt processing backend

System Design: Background Processing Backend for LLM Prompts

Context

Requirements

Constraints & Assumptions

Clarifying Questions to Ask

What a Strong Answer Covers

Follow-up Questions

Submit Your Answer to Earn 20XP