8bit.tr Journal
Evaluation Harness for LLM Products: From Datasets to CI Gates
How to build a reliable evaluation harness for LLM products with datasets, scoring, and automated release gates.
Why an Evaluation Harness Matters
LLM products regress easily when prompts, data, or models change.
An evaluation harness provides a stable signal to prevent silent quality loss.
Dataset Design for Real Workflows
Your dataset should reflect real user tasks, not synthetic trivia.
Include edge cases and adversarial examples to stress the system.
Scoring and Rubrics
Combine automated metrics with human review for ambiguous cases.
Use consistent rubrics to keep evaluation repeatable over time.
Continuous Integration Gates
Run evaluations on every model or prompt change.
Block releases that reduce critical-task performance.
Monitoring Drift in Production
Offline scores do not capture live drift.
Log user corrections and compare them to baseline evaluation results.
Release Criteria and Reporting
Define clear release thresholds for critical tasks and enforce them consistently. A simple pass or fail gate reduces subjectivity and speeds up decisions.
Share evaluation summaries with product and support teams. When everyone understands the results, rollout risk stays low and expectations are aligned.
Keep historical scorecards so trends are visible across releases. Long-term visibility prevents quality drift from going unnoticed.
Tie evaluation outcomes to explicit release owners so accountability is clear when quality slips.
Highlight top regressions and fixes in a short changelog so teams act on the data.
Review evaluation thresholds quarterly to ensure they match current product priorities.
Include representative error examples so teams can understand failures quickly.
Archive evaluation artifacts so past decisions remain auditable.
Rotate evaluation tasks periodically to avoid overfitting the harness to stale datasets.
Add a lightweight executive summary so non-technical stakeholders can follow progress.
Publish evaluation dashboards internally so teams can self-serve insights.
Track evaluation time and cost so the harness stays lightweight enough to run frequently.
Standardize report formats so comparisons across releases remain consistent.
FAQ: Evaluation Harness
How big should the dataset be? Start with 50 to 200 high-value tasks.
Do I need human review? Yes for complex tasks and subjective outputs.
What is the fastest win? Automate a small but high-signal test suite in CI.
About the author
