AI Evaluation Strategy

Overview

AI quality should be measured using repeatable evaluations, not only manual spot checks. A practical evaluation strategy mixes offline test sets with online production metrics.

Evaluation Layers

flowchart TD
    A[Dataset Design] --> B[Offline Evaluation]
    B --> C[Regression Baseline]
    C --> D[Release Gate]
    D --> E[Online Monitoring]
    E --> F[Feedback Loop]

Practical Example - C# Evaluation Model

public sealed record EvalCase(string Input, string ExpectedContains, string Category);
public sealed record EvalResult(string Category, bool Passed, string Output);

public sealed class Evaluator
{
    private readonly ILlmClient _client;

    public Evaluator(ILlmClient client)
    {
        _client = client;
    }

    public async Task<EvalResult> RunAsync(EvalCase test, CancellationToken ct)
    {
        var output = await _client.CompleteAsync(test.Input, ct);
        var passed = output.Contains(test.ExpectedContains, StringComparison.OrdinalIgnoreCase);
        return new EvalResult(test.Category, passed, output);
    }
}

Production Example - Release Criteria

A release can require:

• Minimum pass rate per scenario category
• No regressions in safety or policy tests
• P95 latency within target threshold
• Cost per 1,000 requests within budget

Common Pitfalls

• Evaluating only happy-path prompts
• No baseline to compare model or prompt changes
• Mixing business metrics and quality metrics without clear ownership

Key Takeaways

• Evaluation should be versioned and automated.
• Quality, latency, and cost must be tracked together.
• Regression testing is mandatory before rollout.