AI Evaluation.

AI evaluation asks whether a system performs the intended job on inputs that resemble real use. For frame analysis, this means more than checking whether the response sounds plausible. A caption can be fluent but add unsupported detail; a structured response can have valid fields but wrong values. Define grading criteria before tuning the prompt, include cases where the correct answer is uncertainty, and separate formatting success from factual success. The consequences of a mistake should shape the review threshold.

This archive focuses on practical test design and comparisons that remain meaningful after a demonstration. Track model settings, input preparation, task accuracy, latency, and cost per accepted result. The AI frame guide connects evaluation to the rest of the workflow. Reserve some examples for a final check rather than repeatedly adapting the prompt to every test image. When an output fails, identify whether the cause was missing visual evidence, unclear instructions, model limitations, or inadequate validation.