Evaluating AI language models requires assessing five distinct dimensions: factual accuracy, logical coherence, contextual relevance, completeness, and calibrated confidence. Focusing on just one dimension, like factual accuracy, can lead to misleading quality scores and dangerously wrong outputs.
Relying on general-purpose AI benchmarks for specialized applications like medicine or finance is a critical mistake. Domain-specific benchmarks (e.g., MedQA, Finca) are essential to uncover failures that generic tests would miss, preventing potentially disastrous real-world outcomes.
For tasks involving multi-step logic, evaluating only the final answer is insufficient. True correctness requires process-level evaluation, verifying each step in the AI's reasoning chain. A right conclusion reached through a faulty process is untrustworthy and indicates a model failure.
Treating AI evaluation as a single, pre-launch check is a mistake. Model behavior drifts due to fine-tuning, infrastructure changes, and shifts in user queries. Production AI systems demand a continuous evaluation pipeline integrated into the deployment lifecycle to catch regressions and ensure ongoing reliability.