Design a Hybrid Evaluation Platform
Design a Hybrid Evaluation Platform
Design an evaluation platform for model outputs that supports both human evaluation and automated LLM-based evaluation.
System Requirements
The system should:
- Ingest prompts, model outputs, references, and associated metadata.
- Support multiple evaluation rubrics and various score types.
- Route evaluation tasks dynamically to either human reviewers or LLM judges.
- Collect scores, rationales, and reviewer-specific metadata from both human and LLM evaluators.
- Measure evaluator agreement and assess the quality of collected scores.
- Aggregate evaluation results by model version, task type, and specific data slices.
- Expose aggregated results through interactive dashboards or programmatic APIs.
Discussion Points
Be prepared to discuss the following aspects of your design:
- Overall system architecture.
- Data model for storing evaluation inputs, outputs, and metadata.
- Evaluation workflow, from ingestion to result exposure.
- Strategies for calibrating both human and LLM evaluators.
- Reliability checks to ensure data integrity and system uptime.
- Approaches to handle disagreement between human and automated evaluators.
AI interview prep
Core dilemma
The central engineering conflict is balancing the speed, scalability, and cost-efficiency of automated LLM evaluation with the nuanced judgment, accuracy, and potential for bias of human evaluation, all while ensuring consistent quality metrics and a robust feedback loop for model improvement.
3 tradeoffs and 3 stress tests locked
Interview prep
Unlock the interviewer X-Ray rubric, tradeoff matrix, stress tests, clarifying questions, and key concepts.
Design a Hybrid Evaluation Platform
Design a Hybrid Evaluation Platform
Design an evaluation platform for model outputs that supports both human evaluation and automated LLM-based evaluation.
System Requirements
The system should:
- Ingest prompts, model outputs, references, and associated metadata.
- Support multiple evaluation rubrics and various score types.
- Route evaluation tasks dynamically to either human reviewers or LLM judges.
- Collect scores, rationales, and reviewer-specific metadata from both human and LLM evaluators.
- Measure evaluator agreement and assess the quality of collected scores.
- Aggregate evaluation results by model version, task type, and specific data slices.
- Expose aggregated results through interactive dashboards or programmatic APIs.
Discussion Points
Be prepared to discuss the following aspects of your design:
- Overall system architecture.
- Data model for storing evaluation inputs, outputs, and metadata.
- Evaluation workflow, from ingestion to result exposure.
- Strategies for calibrating both human and LLM evaluators.
- Reliability checks to ensure data integrity and system uptime.
- Approaches to handle disagreement between human and automated evaluators.