Start by clarifying requirements (e.g., real-time collaboration, streaming responses, document persistence) and then present a data model that treats the document as a sequence of blocks, each block being either text or a prompt-response pair. Then describe the component hierarchy and state management, emphasizing how to handle asynchronous updates and rendering inline responses without disrupting the document flow.
Pro tip: Discuss how you would handle streaming responses and partial updates to avoid re-rendering the entire document, perhaps using a normalized state shape and memoization. Also, mention trade-offs between storing responses as separate entities vs. embedding them in the document structure.
Ask questions to understand key constraints: Is the document collaborative? Should responses stream? What's the expected scale? This shows you think about the problem holistically before diving into design.
Propose a document model as an ordered list of blocks, where each block has a type (text or prompt) and content. For prompt blocks, include fields for the prompt text, response, status (e.g., loading, complete), and any metadata. Consider using a normalized store (e.g., byId) for efficient updates.
Outline a component hierarchy: a Document component that renders a list of Block components. Each Block can be a TextBlock or PromptBlock. PromptBlock renders the prompt and, conditionally, a Response component below it. Use a state management solution (e.g., Redux, Zustand) to handle updates.
Explain how to manage API calls for prompt responses, including streaming updates. Use optimistic UI or loading states, and update only the affected block to avoid full re-renders. Discuss error handling and retries.
Compare different approaches: e.g., storing responses inline vs. separately, using a flat vs. nested data model, and implications for collaboration, undo/redo, and performance. Mention how the design supports future features like editing prompts or re-running.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the requirements and constraints of the inline editor, such as expected latency, token throughput, and cancellation semantics. Then propose a streaming architecture using server-sent events (SSE) or WebSockets, with a client-side buffer that incrementally renders tokens and supports cancellation via AbortController. Finally, discuss trade-offs around partial rendering, error handling, and user experience.
Pro tip: Emphasize the importance of graceful degradation: if streaming fails, fall back to a non-streaming response or show a retry option. Also, mention that cancellation should not only stop the stream but also clean up resources on both client and server to avoid leaks.
Ask about expected response size, latency tolerance, and whether the editor supports multiple concurrent streams. Confirm if cancellation should be immediate or allow partial results to persist.
Select between SSE, WebSockets, or fetch with ReadableStream based on bidirectional needs and browser support. Justify your choice with trade-offs like overhead, compatibility, and ease of cancellation.
Implement a buffer that accumulates tokens and triggers UI updates at a throttled rate to avoid excessive re-renders. Use a virtual DOM or direct DOM manipulation for efficient partial rendering.
Use AbortController to cancel the fetch/stream, and ensure the server stops generation and releases resources. On the client, clear buffers and update UI to reflect cancellation.
Define behavior for network failures, timeouts, and partial responses. Consider fallback to non-streaming mode and provide user feedback (e.g., retry button).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying requirements: is the prompt saved for personal reuse or shared within a workspace? Then propose a persistence layer (e.g., a database table) with a flexible ownership model that supports both per-user and per-workspace scoping, and discuss how to enforce access control and query efficiently.
Pro tip: Mention that you'd model ownership with a polymorphic owner (owner_type + owner_id) or separate nullable foreign keys, and add a unique constraint to prevent duplicate prompts per owner. Also highlight the importance of indexing owner columns for fast lookups.
Ask whether prompts can be personal, workspace-shared, or both, and whether users can belong to multiple workspaces. This determines the ownership model.
Select a durable database (e.g., PostgreSQL) for structured metadata and possibly a blob store for large prompt text. Justify based on query patterns and scale.
Propose a prompts table with columns like id, content, created_at, and ownership fields. Use either a polymorphic owner (owner_type, owner_id) or separate user_id and workspace_id with a check constraint.
Add foreign keys, unique constraints (e.g., unique per owner and name), and application-level authorization to ensure users can only access prompts they own or that belong to their workspaces.
Index owner columns and consider partitioning or sharding if needed. Discuss how to handle listing prompts for a user across personal and workspace scopes.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I jumped straight to short links and ACL tables but then they asked about revocation and I realized I hadn't thought through what happens to cached or already-opened links.
Start by outlining the core entities (resources, users, groups, links) and the access control model (ACL/RBAC). Then walk through the sharing flow: how a user selects a scope (public, link, specific users), how permissions are enforced at read/write time, and how revocation propagates. Emphasize trade-offs between simplicity, security, and scalability.
Pro tip: Mention that link-based sharing should use unguessable tokens with optional expiration and that revocation must be immediate—consider a centralized permission service or token blacklist to avoid stale caches. Also note that public sharing should be explicit and audited.
Choose between ACLs, RBAC, or a hybrid. Explain how permissions are stored (e.g., per-resource ACL) and inherited (e.g., folder-level).
Cover public (anyone), link-based (anyone with link), and specific users/groups. For link-based, discuss token generation, expiration, and optional password.
Detail how checks happen at request time: authenticate user, resolve effective permissions (considering groups, inheritance), and authorize action. Mention caching and invalidation.
Describe how to revoke access: remove ACL entries, invalidate link tokens, and ensure propagation (e.g., via pub/sub or short TTL caches). Discuss immediate vs eventual consistency.
Address scalability, performance, security (e.g., token leakage), and usability (e.g., accidental public sharing). Mention audit logs and monitoring.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Honestly the question I was least prepared for.
Start by clarifying the scope—distinguish between data included in the prompt itself versus data used for model training or logging—then outline a privacy-by-design pipeline that classifies, redacts, and governs sensitive data. Emphasize trade-offs between utility and privacy, and propose concrete mechanisms like PII detection, differential privacy, and user controls.
Pro tip: Show you understand that privacy isn't just about redaction—it's about data minimization, purpose limitation, and giving users transparency and control. Mention that you'd design for auditability and compliance from day one, not as an afterthought.
Define what 'prompt data' includes: user input, system instructions, conversation history, metadata (timestamps, user IDs), and any retrieved context. Distinguish between data processed in real-time versus data stored or used for training.
Discuss types of sensitive data (PII, PHI, credentials, proprietary info) and risks like memorization, leakage, or unauthorized access. Consider both user-provided data and model-generated responses that might contain sensitive context.
Propose layered defenses: PII detection and redaction, data minimization, encryption, access controls, and techniques like differential privacy or federated learning. Explain how these apply at ingestion, processing, and storage stages.
Outline policies for data retention, user consent, opt-outs, and transparency (e.g., data usage dashboards). Include audit trails and compliance with regulations like GDPR or CCPA.
Acknowledge trade-offs between privacy, utility, and performance. Suggest metrics (e.g., false positive/negative rates for PII detection) and a feedback loop to improve over time.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Token bucket per user, hard caps per workspace, async usage tracking.
Start by clarifying the system's requirements: expected traffic, user tiers, and cost constraints. Then propose a multi-layered approach combining rate limiting (e.g., token bucket per user/API key) and cost controls (e.g., budget quotas, token counting, caching). Emphasize trade-offs between user experience, fairness, and cost efficiency, and mention monitoring and dynamic adjustments.
Pro tip: Show awareness of OpenAI's specific challenges: handling streaming responses, token-based rate limits, and the need for graceful degradation. Mention that rate limiting should be applied at multiple levels (user, organization, global) and that cost control includes optimizing prompts and using cheaper models for certain tasks.
Ask about expected request volume, user tiers, cost budget, and latency requirements. Understand if rate limiting is per user, per API key, or global, and whether cost control is about preventing abuse or managing expenses.
Propose algorithms like token bucket or sliding window for rate limiting. Consider multiple dimensions: requests per minute, tokens per minute, and concurrent requests. Implement at API gateway and application layers.
Use token counting to estimate costs before processing. Set budget quotas per user/organization. Implement caching for repeated queries and suggest cheaper models for non-critical tasks. Monitor and alert on cost anomalies.
Define behavior when limits are exceeded: return 429 with retry-after, queue requests, or degrade gracefully. Ensure distributed rate limiting using Redis or similar. Handle streaming responses by counting tokens as they arrive.
Set up logging and metrics for rate limit hits, cost per user, and system load. Use this data to adjust limits dynamically and optimize costs. Consider A/B testing different limits to balance user experience and cost.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.