Intelligent Routing for Specialized AI Model Portfolios

Routing decisions rely on trustworthy evaluation data. Build an evaluation apparatus that tests models across the scenarios you intend to support.

Evaluation Suite Components#

• Golden Sets: curated datasets representing critical user intents, regulatory contexts, and failure scenarios.
• Synthetic Scenarios: generated workloads using controllable templates to stress reasoning depth, tool usage, or multi-turn dialogue.
• Human Review Panels: domain experts scoring outputs for accuracy, tone, compliance, and usefulness.
• Behavioral Analytics: telemetry from production that highlights real usage patterns, unmet needs, and emerging edge cases.

Define evaluation stages—smoke tests for onboarding, regression suites for updates, periodic audits for drift, and red-team exercises targeting safety vulnerabilities.

Scoring Dimensions#

• Quality (accuracy, coherence, creativity, factuality)
• Responsibility (bias mitigation, harmful content avoidance, privacy adherence)
• Cost (tokens per task, inference time, GPU-hours)
• Reliability (completion rate, tool invocation success, resource usage variance)

Use composite scores with transparent weights, but retain granular metrics. Routing controllers often require raw dimensions to make nuanced trade-offs.

Evaluation Cadence#

Establish a calendar: daily smoke tests, weekly regression sweeps, monthly domain audits, quarterly safety stress tests. Automate baseline comparisons and trend analysis so anomalies trigger alerts.