THE SHORT ANSWER
An AI video summary should not be stored as one fluent paragraph. Keep URL, publication date, file or version, transcript language and extraction time; attach timestamps and visual evidence to material findings. Separate creator words, visible facts, team inference and model summary. Use short verifiable excerpts, and have humans open the source for product claims, brand safety and recommendation rationale.
Fix source version and transcript context
Record video URL, account, date, duration, extraction date and available file hash. Content may be edited, removed or reuploaded, so a URL alone cannot prove the reviewed version.
Label transcript language, automated or human origin, speaker and inaudible segments. Music, on-screen text and actions are not captured by transcript alone.
Link material findings to timestamps
Product appearance, experience, comparison, disclosure, call to action and risk signals need start-end timestamps. Save keyframes or concise visual observations so model inference does not become fact.
“Creator demonstrates setup at 02:14–02:31 and says it takes about ten minutes” is more reviewable than “provides a detailed product introduction.”
Limit quotations and label inference
Keep only short excerpts needed to support a finding and link to the source. Attribute opinions to the creator rather than rewriting them as brand fact. Audience, intent or sentiment judgments are labeled as inference with confidence.
Low-confidence speech, sarcasm, multiple speakers and translation require human review. Models should not infer sensitive personal traits from appearance for selection.
Audit summary quality with stratified samples
Sample by language, duration, platform and risk, then count omission, fabrication, wrong timestamp and wrong attribution separately. Ten short English videos do not validate long live streams or other languages.
QuickKOL stores summary separately from evidence cards. After model or rule changes, rerun a fixed benchmark set to see whether errors actually decline.
Use AI to locate evidence, not replace viewing
Video analysis can first identify shots, captions, entities, product appearances and topic timecodes, then a reviewer checks segments relevant to the selection job. YouTube Advanced Mode provides performance comparisons across videos and groups, while platform metrics still need semantic context: a high-retention segment may be a useful story turn or unrelated controversy.
For a 12-minute video, segment every 30 seconds, let AI mark demonstration, explanation, claim, disclosure and CTA, then review every high-risk claim and at least 20% of ordinary segments. Output timecode, transcript, capture, model version, confidence and human conclusion. Missing captions, mixed languages or unclear visuals stay unknown rather than being invented.
AI video evidence record
Every conclusion returns to an original timecode.
| Field | Content | Purpose |
|---|---|---|
| Location | URL, timecode, capture | Return to source |
| Extraction | Transcript, entity, scene | Human review |
| Model | Version, date, confidence | Understand limits |
| Conclusion | Confirmed, rejected, unknown | Decide use |
CHECKLIST
AI video evidence QA
Complete before evidence enters the creator record.
- Original video and timecode are accessible
- Caption language and version are recorded
- All high-risk claims are human-reviewed
- Ordinary segments have representative sampling
- Captures preserve context
- Model confidence is not treated as fact
- Missing information remains unknown
- Conclusion has reviewer and date
Sources and further reading
External pages can change. Recheck platform rules, contracts and local law before launch.