daBongo LMS AI Training Courses

Lesson Content

Defining quality acceptance criteria.

Quality acceptance criteria define what "good enough for delivery" means for a specific project – rather than an indefinite standard that leads to infinite iteration. Criteria should cover: visual quality level required, temporal consistency standards, acceptable motion artifacts, and any content-specific requirements (e.g., a recognizable specific visual element must appear correctly). Criteria defined before generation begins prevent scope creep in review.

Structured quality review checklist.

A professional review checklist for AI-generated video covers:

1. Temporal consistency – does visual style remain stable across all frames?
2. Motion quality – is movement smooth and intentional, not jerky or randomly drifting?
3. Prompt adherence – does the clip match the intended visual description?
4. Technical specifications – correct resolution, frame rate, and file format for delivery?
5. Content quality – no visible generation artifacts (unnatural morphing, deformed elements)?

Common AI video quality issues.

Issues requiring particular attention in AI-generated video: temporal flickering (visual style inconsistency frame to frame), unnatural morphing of details (faces, text, hands), camera motion drift (unintended camera movement), and content hallucinations (elements appearing that weren't specified and are wrong for the scene). Each has characteristic remediation approaches – temporal flickering may be addressed by model selection; unnatural morphing by regeneration with different settings.

Quality gate before delivery.

Establish a required sign-off step (a reviewer who is not the generator) before any AI-generated clip goes to client – the generation process creates familiarity bias in the creator, making them less likely to notice quality issues that a fresh reviewer would catch.