Pick one production workload to baseline.
Optimization works best when isolated to a single repeated task with a measurable definition of quality. This diagnostic maps your workload pattern, eval maturity, operational bottlenecks, and fallback strategy to identify the highest-leverage path forward.
Workload Optimization Diagnostic
Workload Profile & Optimization Route
Signal breakdown: Supporting signals indicate whether your pipeline is ready for automated evaluation, dataset distillation, or tiered routing.
Execution prerequisites
Production guardrails & fallback
Capture → Evaluate → Promote.
Architecture, Integration & Security
How does Understudy integrate with our existing codebase?
Understudy operates via a standard OpenAI-compatible gateway proxy, local SDK client wrapper, or CLI workbench. You do not need to rewrite your agent logic or migrate pipelines—point your endpoint base URL or wrap your client to start capturing traces and evaluating routes.
What is an evaluation ("eval") harness?
An eval harness is an automated regression test suite for model outputs. It combines deterministic checks (JSON schema validation, regex, type constraints), programmatic domain assertions, and frozen golden datasets to score candidate models before deployment.
How does Understudy prevent quality regressions and silent failures?
Quality gating and tiered fallback. A candidate model or prompt only receives live traffic when it clears the held-out eval bar. If the specialized route encounters high uncertainty or a schema error, it automatically escalates to your frontier model baseline.
Where are models hosted and who owns the fine-tuned weights?
You own all resulting assets: prompts, evaluators, routing configurations, and specialized model weights. Models can be served within your private VPC (AWS Bedrock, GCP Vertex, Fireworks, vLLM) or via managed inference.
How does this differ from generic prompt caching or semantic routers?
Caching only accelerates exact or near-identical queries. Understudy optimizes the entire execution route: prompt restructuring, tool-calling adapters, parameter compression, task-specific distillation, and dynamic complexity routing against measured evals.
Do we need weeks of labeling or custom training before seeing gains?
No. 50–100 production traces and 3–5 core validation assertions are sufficient to establish the first baseline. Major initial latency and cost gains often come from prompt optimization, structured output constraints, and tiered routing before model fine-tuning.