A common method for initial model evaluation is the "Vibe Check"—a manual, ad-hoc conversation with the bot to see if it "feels" smart. While useful for drafting, this is insufficient for deployment.
The Challenge: Quantifying "Fluency" vs "Correctness."
LLMs are excellent at sounding confident. However, a model can be fluent while being factually incorrect. Manual spot-checking covers only a tiny fraction of potential user inputs.
Recommendation: Deterministic Eval Suites.
We advocate for Automated Scorecards. Before any update reaches production, it should pass a suite of 100+ real-world questions with known "Golden Answers." This transforms quality from a subjective feeling into an objective metric (e.g., "94% Accuracy on Q3 Benchmark").