Stripe AI Interview Questions

Structured JSON output from LLMs is a production necessity at Stripe, Vercel, and every AI-native startup. Interviewers test your reliability engineering — schema enforcement, retry logic, streaming edge cases — not just 'use JSON mode.' Master the full production playbook.

Hard orchestration comparison — LangGraph agent graphs vs durable workflow engines.

Rate limiting for agents differs from human API usage — bursts are larger and less predictable.

MCP testing maturity separates hobby servers from production integrations.

Agents waste tokens and cause incidents when errors are opaque strings.

Database MCP is high risk/high value — interviewers probe your safety instincts hard.

This question tests integration strategy, not religious protocol loyalty.

Tool schema drift breaks agents silently — models call old argument shapes and get 400s. Interviewers want a semver-minded process.

Hybrid designs where LLMs handle language but calculators, rules engines, and APIs enforce deterministic outcomes.

Centralized prompt storage with versioning, approvals, environment promotion, audit trails, and runtime serving.

Schema-first extraction from PDFs, emails, and contracts with validation, chunking, and merge strategies.

Protect instructions, secrets, policy, and tool policies in system prompts against leakage, override, and indirect injection.

MLOps for prompts: semantic versioning, eval gates, traffic splitting, and rollback when prompt changes regress quality or cost.

Hard project question on LLM gateway — auth, routing, rate limits, logging, key management, and multi-provider abstraction.

Scenario question on offline golden eval vs online A/B, feedback, and guardrail metrics in production.

Scenario question on resilient LLM clients — exponential backoff, 429 handling, idempotency keys, and duplicate-safe writes.

Hard scenario on LLM cost control — token budgeting, routing, caching, batch APIs, and unit economics at millions of queries.