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How to Evaluate Small Language Models for Production

OpenTracy Team//
guideevaluation

A comprehensive guide to evaluating SLMs, including metrics, test sets, and common pitfalls to avoid.

Deploying a Small Language Model to production requires rigorous evaluation. Here's our framework for ensuring quality.

Define Success Metrics

Before evaluating, define what "good" means for your use case:

  • Accuracy: Does the model give correct answers?
  • Latency: How fast are responses?
  • Consistency: Are outputs stable across similar inputs?
  • Safety: Does the model avoid harmful outputs?

    • Build a Test Set

    Your test set should represent real production traffic:

  • Sample from production logs
  • Include edge cases and failure modes
  • Cover all major use case categories
  • Update regularly as your product evolves

    • Evaluation Methods

    Automated Metrics

  • Exact match accuracy
  • Semantic similarity scores
  • Latency percentiles (p50, p95, p99)

    • Human Evaluation

  • Blind A/B testing against the teacher model
  • Quality ratings on a defined rubric
  • Error categorization

    • Production Monitoring

  • Shadow deployment comparisons
  • Gradual rollout with monitoring
  • Automatic rollback triggers

    • Common Pitfalls

  • Overfitting to the test set: Regularly refresh your evaluation data
  • Ignoring edge cases: Specifically test failure modes
  • Optimizing a single metric: Balance accuracy, latency, and cost

    • OpenTracy's Evaluation Suite

    OpenTracy automates much of this evaluation process, providing comprehensive quality reports before deployment.