System Design Prompt: Internal Employee Interest Matching Platform
Context
Design an internal-only platform that connects employees with similar interests to foster collaboration, mentorship, and community. Assume a typical enterprise environment with Single Sign-On (SSO), an HR directory, and tens of thousands of employees. The system should provide near real-time updates when interests change.
Requirements
Features
- Profile creation and editing
- Interest tagging (free text with normalization and a suggested taxonomy)
- Discovery and search (by tags, location, team, etc.)
- Matching/recommendations (who should I connect with?)
- Messaging (1:1 and small group)
Design Considerations
- Data model and storage choices
- APIs (profile, interests, discovery, recommendations, messaging)
- Matching and ranking approach (signals, algorithm)
- Privacy and access controls (internal-only visibility, role/attribute-based)
- Cold start strategy
- High-level architecture (services, databases/search indices, cache, event/queue)
- Scaling plan to tens of thousands of employees with near real-time updates
- Monitoring/metrics and abuse prevention
AI interview prep
Core dilemma
Balancing the computational complexity and data freshness of a personalized, real-time interest matching and recommendation system for a large enterprise, while ensuring scalability, data privacy, and robust operational stability.
3 tradeoffs and 3 stress tests locked
Interview prep
Unlock the interviewer X-Ray rubric, tradeoff matrix, stress tests, clarifying questions, and key concepts.
System Design Prompt: Internal Employee Interest Matching Platform
Context
Design an internal-only platform that connects employees with similar interests to foster collaboration, mentorship, and community. Assume a typical enterprise environment with Single Sign-On (SSO), an HR directory, and tens of thousands of employees. The system should provide near real-time updates when interests change.
Requirements
Features
- Profile creation and editing
- Interest tagging (free text with normalization and a suggested taxonomy)
- Discovery and search (by tags, location, team, etc.)
- Matching/recommendations (who should I connect with?)
- Messaging (1:1 and small group)
Design Considerations
- Data model and storage choices
- APIs (profile, interests, discovery, recommendations, messaging)
- Matching and ranking approach (signals, algorithm)
- Privacy and access controls (internal-only visibility, role/attribute-based)
- Cold start strategy
- High-level architecture (services, databases/search indices, cache, event/queue)
- Scaling plan to tens of thousands of employees with near real-time updates
- Monitoring/metrics and abuse prevention