Also reported as Design a Prompt Playground · Prompt Playground Design · Design ChatGPT
Design a prompt engineering playground platform similar to Anthropic Console. This is a full-stack design covering product features, UI/UX, backend architecture, data modeling, and scalability. Key constraints: (1) Each run is independent — the system does NOT maintain conversation context between runs (not a chatbot). (2) The system should record/persist previous prompts and runs. (3) Users can share prompts/conversations. (4) Prompts can be very large (up to 10MB+). The interview is typically conducted as a Google Doc-style discussion rather than whiteboard/diagram drawing. Interviewers probe: core entities and relationships, indexing strategy for large prompts, what to store in S3 vs DB, client-side performance when many windows are open each with large prompts, scalability via message queues, sharing mechanism, and UX/user flow.
Naive approach with serious trade-off — being authored.
Solid baseline with reasonable trade-offs — being authored.
Production-grade approach with explicit trade-off rationale — being authored.
Naive approach with serious trade-off — being authored.
Solid baseline with reasonable trade-offs — being authored.
Production-grade approach with explicit trade-off rationale — being authored.
Naive approach with serious trade-off — being authored.
Solid baseline with reasonable trade-offs — being authored.
Production-grade approach with explicit trade-off rationale — being authored.
Naive approach with serious trade-off — being authored.
Solid baseline with reasonable trade-offs — being authored.
Production-grade approach with explicit trade-off rationale — being authored.
Naive approach with serious trade-off — being authored.
Solid baseline with reasonable trade-offs — being authored.
Production-grade approach with explicit trade-off rationale — being authored.
Cover happy path. Clarify scope. Identify the obvious bottleneck. Pick a reasonable storage and reasonable scaling approach.
All of the above plus: explicit failure handling, durability vs latency trade-offs, choose the right batching/caching strategy, articulate why.
All of the above plus: organizational concerns (rollout, migration, on-call), quantitative analysis, multi-region considerations, what could go wrong with the proposed solution at 10x scale.
Common mistakes: Not writing enough content in the shared doc during the interview — failing to document design as you talk; Getting trapped by low-level detail questions (interviewer 'traps') without anchoring back to high-level design; Not proactively addressing large prompt (10MB+) scenario — waiting for interviewer to force it; Poor S3 vs DB storage decision rationale — no clear threshold or trade-off articulation; Treating the system as a stateful chatbot with conversation context instead of independent runs; Not covering UX/user flow sufficiently — over-indexing on backend infrastructure; Putting all user request types into a single message queue, ignoring tier-based prioritization; Not preparing for sharing functionality as a distinct sub-system with its own data model
Interviewer hints: Interviewer stayed silent when candidate said all events go in one queue — implying paid/free tier queues should be separated; Interviewer kept redirecting discussion back to large prompt handling and S3 placement when candidate moved on; Some interviewers actively typed notes in the shared doc to help candidate stay focused on thinking rather than writing; Interviewer did not ask candidate to draw diagrams — confirmed pure Google Doc discussion format
What passers do: Proactively raised large prompt handling before interviewer asked; Wrote extensively in shared doc throughout discussion, not just verbally explaining; Clearly defined S3 threshold and articulated why (row size limits, replication cost, streaming); Covered both frontend UX (virtual scroll, lazy load) and backend architecture in balanced depth; Distinguished between independent runs and chatbot context explicitly upfront; Discussed trade-offs between approaches rather than presenting one solution as correct
Why people fail: Designed it like a stateful chatbot instead of independent-run playground; Ignored or glossed over large prompt storage; only addressed after interviewer repeatedly prompted; Did not write anything in the doc — only talked, leaving no artifact for interviewer to reference in feedback; Could not articulate when to use S3 vs DB with a defensible threshold; Spent all time on backend, completely neglected UX/user flows which are explicitly evaluated; Could not discuss message queue trade-offs when asked about scale
Edge cases probed: Prompts exceeding 10MB — chunked upload, S3 storage, streaming read; Many browser windows open simultaneously each with large prompts — client memory pressure; S3 vs DB placement decision and the threshold criteria; Sharing a mutable prompt — snapshot vs live reference consistency; Concurrent edits to the same prompt (collaborative editing or lock mechanism); Rate limiting when GPU cluster capacity drops suddenly — dynamic tightening of rate limits; Paid vs free user queue prioritization for async runs
Alternative approaches: Sync HTTP API for runs (no queue) (Simpler architecture, lower latency for short prompts; but ties up API server threads during LLM inference, poor for long-running or high-concurrency scenarios.); Store all prompts inline in DB (no S3) (Simpler, no external dependency; but DB row size limits, performance degrades for very large blobs, expensive for backup/replication of large text.); Vector DB for prompt caching/similarity search (Enables semantic deduplication and similar-prompt suggestions; adds operational complexity, requires embedding pipeline, KV store is sufficient for exact-match caching.); WebSocket vs SSE for streaming output (WebSocket is bidirectional (supports cancellation), SSE is simpler unidirectional and easier to proxy; SSE preferred for streaming LLM tokens, WebSocket if cancel/interrupt is needed.)
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