This one took me a second to get my footing on.
Start by clarifying the product's requirements and constraints, then systematically evaluate models based on cost, latency, quality, and scalability. Propose a hybrid architecture that routes requests dynamically, and emphasize the importance of measuring and iterating based on real-world data.
Pro tip: Quantify trade-offs with concrete metrics (e.g., cost per 1K tokens, p95 latency) and propose a fallback mechanism to handle model failures or degradation, showing you think about reliability and cost predictability.
Identify the key user experience needs: acceptable latency, required output quality, expected traffic volume, and budget constraints. Consider whether the task is latency-sensitive (e.g., real-time chat) or can tolerate delays (e.g., batch processing).
Compare models on dimensions like cost per token, latency (p50/p95), output quality (accuracy, coherence), and rate limits. Use benchmarks and A/B tests to quantify these factors for your specific use case.
Propose a routing layer that directs requests to the appropriate model based on criteria such as query complexity, user tier, or real-time load. For example, use the frontier model for high-stakes or complex queries and the cheaper model for simple or bulk requests.
Set up metrics to track cost, latency, and quality in production. Use this data to continuously refine routing rules and model selection, and to detect when a cheaper model's quality is sufficient.
Ensure the system can handle increased load by scaling the cheaper model and using the frontier model sparingly. Implement fallback mechanisms to switch models if one fails or exceeds latency thresholds.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.