← Perplexity Interview Insights
This is a single question but it's basically six questions duct-taped together.
Start by clarifying requirements and scale, then walk through the high-level architecture covering data model, API, LLM integration, sync, and offline support. Emphasize trade-offs and how each component handles failures and edge cases, especially around natural language parsing and conflict resolution.
Pro tip: Treat the LLM as an unreliable service: design a fallback parser and cache LLM outputs to reduce cost and latency. Also, discuss how you'd evaluate and improve the LLM's accuracy over time using user corrections.
Ask about expected user base, task volume, latency requirements, and offline usage patterns. Confirm whether the app is consumer or enterprise, and if there are compliance needs (e.g., GDPR).
Define core entities (User, Task, Subtask, Reminder, Schedule) and their relationships. Sketch REST or GraphQL endpoints for CRUD operations, natural language input, and sync.
Explain how to parse natural language into structured tasks using an LLM (e.g., function calling), with fallback to rule-based parsing. Describe how to generate subtask suggestions and smart prioritization, including caching and rate limiting.
Choose a sync strategy (e.g., CRDTs or operational transforms) and describe conflict resolution. Detail offline-first architecture with local storage, background sync, and queueing of LLM requests when offline.
Discuss scaling the backend (sharding, caching), LLM cost/latency mitigation, and monitoring. Highlight trade-offs between consistency and availability, and between LLM accuracy and fallback mechanisms.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Talked through storing recurrence as a rule object separate from the task instance, something like an RFC 5545 style pattern.
Start by clarifying the requirements: what types of recurrence rules are needed (daily, weekly, monthly, custom), how flexible they must be, and the expected scale. Then propose a data model that separates the recurrence rule definition from the task instances, using a structured format like RRULE or a custom DSL, and discuss trade-offs between storing precomputed instances versus generating them on the fly.
Pro tip: Mention the importance of handling edge cases like time zones, daylight saving time, and exceptions (e.g., skipped occurrences) early in the design, as these often cause bugs in production systems.
Ask about the types of recurrence patterns needed, the scale of tasks, and whether real-time generation or precomputation is preferred. This ensures the design meets actual needs.
Decide between using a standard like iCalendar RRULE, a cron expression, or a custom JSON schema. Discuss the trade-offs in expressiveness, complexity, and ease of querying.
Propose tables for tasks, recurrence rules, and task instances (if precomputed). Include fields for rule parameters, start/end dates, and exception handling.
Explain how to handle time zones, DST, exceptions, and how the model scales with many tasks. Discuss indexing and query patterns.
Compare precomputing instances vs. generating on the fly, and consider hybrid approaches. Mention how the choice affects performance, storage, and complexity.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the product's requirements and constraints, then systematically address each concern (prompt design, latency, cost, privacy) with specific strategies and trade-offs. Emphasize a user-centric approach that balances performance, cost, and privacy while maintaining answer quality.
Pro tip: Quantify trade-offs with concrete examples (e.g., 'Caching can reduce latency by 50% but may serve stale answers') and mention Perplexity's focus on real-time, accurate answers to show alignment with company values.
Ask about expected query volume, latency SLAs, budget constraints, and privacy regulations (e.g., GDPR). This ensures your answer is tailored to the product's needs.
Discuss prompt engineering techniques like few-shot learning, chain-of-thought, and system prompts to improve accuracy. Mention iterative testing and versioning to manage prompt changes.
Propose strategies such as model distillation, quantization, caching frequent queries, and streaming responses. Consider using smaller models for simpler tasks and routing to larger models only when needed.
Suggest cost-saving measures like caching, batching, using spot instances, and monitoring token usage. Discuss trade-offs between cost and quality, and consider fine-tuning smaller models for specific tasks.
Outline data anonymization, encryption in transit and at rest, and compliance with regulations. Mention options like on-premise deployment or using privacy-preserving techniques (e.g., federated learning) if applicable.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the application's data model and user expectations for offline and sync, then propose a client-side storage solution (e.g., IndexedDB) with a sync engine that handles conflicts via a strategy like last-write-wins or CRDTs. Discuss trade-offs between consistency, latency, and complexity, and outline how you'd test and monitor the system.
Pro tip: Emphasize that offline support is not just about caching but about designing for eventual consistency and graceful conflict resolution; mention that you'd start with a simple sync protocol and iterate based on user feedback and metrics.
Ask about the types of data, expected offline duration, number of devices, and consistency needs. This ensures the solution aligns with actual use cases.
Propose using IndexedDB or similar for local storage, and outline a sync engine that queues changes and syncs when online, with conflict resolution.
Discuss options like last-write-wins, version vectors, or CRDTs, and justify your choice based on data types and user experience.
Explain how the server will handle sync requests, including endpoints for delta updates, authentication, and idempotency.
Describe how you'd test offline scenarios, monitor sync success rates, and iterate on the design based on real-world usage.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.