Introduction

BrowserStack AI Evals is a comprehensive platform for testing, observing, and improving AI-powered applications. Whether you're building a simple LLM-powered feature or a complex multi-step agent, AI Evals gives you the tools to understand what your AI is doing, measure whether it's doing it well, and systematically improve it over time.

AI applications are harder to test than traditional software. Outputs are probabilistic, quality is subjective, and regressions are subtle. AI Evals brings software engineering rigour to AI development — giving you the observability of distributed tracing combined with the structured quality measurement of an evaluation framework.

Key Capabilities

LLM Observability Capture every LLM call in your application as a structured trace: inputs, outputs, token usage, latency, model parameters, and any metadata you want to attach. Traces are organized into a hierarchy — sessions, traces, and observations — so you can see the full picture of any request.

Automated Evaluation Score traces and LLM responses automatically using built-in evaluators (RAGAS metrics, LLM-as-judge, rule-based checks) or custom evaluators you define. Evaluations run online (on live traffic) or offline (on datasets) without changing your application code.

Experimentation Build datasets of representative test cases and run them through your AI pipeline as structured experiments. Compare prompt versions, model upgrades, or architectural changes with quantitative scores so you can make decisions with confidence.

Human Review Route traces and evaluation results to a review queue for human annotation. Combine human judgment with automated scoring to build a high-quality feedback loop.

How It Works

The platform has three layers:

Your Application
      │
      │  SDK (Node.js / Python / Java)
      │  captures traces automatically
      ▼
BrowserStack AI Evals API
      │
      │  stores traces, runs evaluations,
      │  manages datasets & experiments
      ▼
Dashboard
      │
      │  explore traces, review scores,
      │  compare experiments, annotate data

You install a small SDK into your application. The SDK intercepts LLM calls and sends structured trace data to the API. From there, evaluators run automatically or on demand, and you explore results in the dashboard.

There is no agent to run, no infrastructure to manage, and no changes required to your LLM provider configuration.

Next Steps