Vercel AI SDK Output Evaluations

Overview

When you change a system prompt or upgrade a model, how do you know if the output actually improved? Manual testing doesn't scale, and subjective "vibes" are a terrible way to measure engineering success.

Our Vercel AI SDK Output Evaluations service builds automated, data-driven evaluation pipelines. We score your agent's responses against golden datasets, ensuring you can deploy updates to production with absolute confidence.

Key Capabilities

1. Automated Evaluation Pipelines
   We integrate evaluation frameworks (like Braintrust or LangSmith) directly into your Vercel AI SDK codebase to automatically grade responses during CI/CD.

2. Custom Scoring Metrics
   We design specific rubrics for your use case, evaluating the LLM for factual accuracy, adherence to brand tone, and exact JSON formatting.

3. Continuous Monitoring
   We set up feedback loops where user interactions (thumbs up/down) feed back into your evaluation dataset, constantly improving your baseline over time.

Why Partner With Us?

• Data-Driven Engineering: We replace subjective testing with hard metrics, allowing your team to iterate faster and safer.
• Deep Integration: We hook the evals directly into the SDK's telemetry and response pipelines.
• Cost Management: We ensure that moving to a cheaper model doesn't secretly destroy your response quality before you commit to the switch.

Treat your AI outputs like standard software tests. Let us build your evaluation pipeline.