Measure the errors specific to each input type.
1. Dataset variation
Include lighting, blur, noise, accents and ambiguous scenes relevant to the product. A pristine demonstration does not represent field conditions.
2. Failure categories
Separate capture failure, preprocessing loss, model misunderstanding and validation failure. This tells you whether to improve the camera flow, prompt or domain checks.
3. Operational budget
Measure upload size, latency and cost for the complete interaction. Reducing model time alone may not help when media transfer dominates.
Worked scenario
Most failures come from unreadable source images rather than model reasoning. A capture-quality hint is more effective than another prompt revision.
Apply it
Create a multimodal evaluation report with per-condition results and a proposed product improvement.
Check your understanding
You can attribute the main errors to the correct part of the pipeline. Explain the decision and show evidence from your implementation or design. If you cannot demonstrate it yet, revisit the relevant section before continuing.