Eval-Driven Development as Test-Driven Development for AI

Building reliable AI systems requires shifting from ad-hoc prompt tweaking to systematic evaluation workflows. The pattern mirrors test-driven development: define evaluation criteria upfront, create a representative test set, iterate on prompts or RAG configuration, re-run evaluations, and deploy only when metrics improve. This prevents regressions where a prompt change fixes one case but breaks three others. Without this discipline, teams waste cycles chasing anecdotal improvements that don't generalize.

The test set becomes your ground truth. It should cover edge cases, common failures, and representative user queries. Version control both prompts and test sets together so you can trace which prompt version performed best on which dataset.

Offline and Online Evaluation: Catching Different Failure Modes

Offline evaluation runs test suites before deployment using static datasets. It catches obvious breaks like format errors, hallucinations on known facts, or response length violations. Online evaluation happens in production through A/B testing, user feedback signals like thumbs up/down, or RLHF (Reinforcement Learning from Human Feedback) pipelines that learn from real user interactions.

Evaluation Layer Comparison

You need both layers. Offline evals are your safety net for catastrophic failures. Online evals surface subtle UX issues that only emerge with real user behavior patterns you couldn't anticipate in test sets.

Cost-Quality Tradeoffs: Layering Evaluation Methods

Human evaluation provides nuanced judgment but costs $50-200 per hour depending on domain expertise. GPT-4 as a judge costs pennies per evaluation but may miss edge cases or apply inconsistent criteria. Heuristic evaluation using keyword matching or length checks runs instantly but only catches surface-level issues.

The strategy is layering based on risk tolerance. Run heuristics first to filter obvious failures. Use GPT-4 eval for the remaining candidates to assess quality dimensions like helpfulness or coherence. Reserve human evaluation for high-stakes decisions, final validation, or building ground truth datasets that calibrate your automated evals. A healthcare chatbot needs more human review than a product recommendation system.

Human-in-the-Loop for High-Stakes Domains

In domains like healthcare, legal, or financial services, human review isn't optional. Design workflows where AI flags uncertain cases for human escalation based on automated confidence scores. If your model-as-judge gives a response a quality score below 0.7, route it to a human reviewer before showing it to the end user.

This scales better than reviewing everything. The AI handles routine cases with high confidence. Humans focus on ambiguous or risky situations where their judgment matters most. Track what percentage of cases get escalated over time as your model improves, this metric tells you if your AI is getting more reliable or if your confidence calibration is broken.