This question is basically six questions stapled together.
Start by clarifying requirements and scale, then propose a modular architecture with separate services for task ingestion, routing, evaluator management, and aggregation. Walk through each component, emphasizing trade-offs between human and LLM evaluators, and conclude with API design and SLA considerations.
Pro tip: Emphasize the importance of a feedback loop where LLM judgments are periodically validated against human reviews to maintain quality and reduce cost over time.
Ask questions to understand expected throughput, latency SLAs, cost constraints, and the mix of human vs. LLM evaluators. This informs architectural decisions.
Outline services for task ingestion (APIs, queues), routing (based on task type, cost, availability), evaluator pool management (registration, skills, availability), and consensus aggregation (voting, weighted scores).
Describe mechanisms like gold-standard tasks, inter-rater reliability, and LLM confidence scores to ensure quality. Explain aggregation methods (majority vote, average, weighted) and conflict resolution.
Discuss scaling strategies (horizontal scaling, auto-scaling), prioritization queues, and fallback mechanisms (e.g., switch to LLM if human pool is saturated) to meet SLAs.
Propose RESTful APIs for task submission, status checks, and result retrieval. Discuss cost optimization by routing simple tasks to LLMs and complex ones to humans, and caching results.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.