4 steps · 96 min · Intermediate
AI Content Quality System
Create a traceable source asset, quantify transcript errors, audit derivative claims, and verify any outside evidence before publication.
Best for: Editors, video teams, researchers, educators, and content operators using hosted or local AI tools.
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Create a traceable local transcript package
How to Transcribe Video Locally With MLX Whisper
Use FFmpeg and MLX Whisper on an Apple-silicon Mac to select a video audio track, export TXT/SRT/VTT/TSV/JSON, and catch wrong-track and caption-break errors.
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Measure word errors and protect meaning
How to Measure AI Transcript Accuracy Before Publishing
Measure transcript accuracy with a no-code or Python WER check, a worked example, and practical checks for missing references, numbers, names, captions, speakers, and silence.
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Audit derivative claims against the source
How to Audit AI Claims Against Supplied Source Facts
Compare an AI draft with the source passages you provide, catch unsupported numbers and overbroad wording, and leave each claim with a clear edit or verification step.
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Verify external claims before release
How to Fact-Check ChatGPT Answers
Follow a real ChatGPT Search answer through NASA source checks, then use the same claim-by-claim method for changing product facts and mixed claims.