Dev cuts AI video errors by 60% using a two-layer lint system before LLM retries

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A developer building Crimetube, an automated documentary video pipeline, found that roughly 1 in 10 AI-generated shots violated consistency rules across 200-plus shots per video. His initial fix of regenerating entire shot clusters was effective but slow and costly, often reprocessing correct shots alongside faulty ones. He replaced this with a 23-rule lint pass that first applies deterministic, code-based corrections — such as regex fixes and pattern matching — at no extra cost. Only shots that still fail after this layer are sent back to the LLM for targeted, single-shot regeneration. The approach reduced rule violations per cluster from around 20 to under 8, while significantly cutting the number of LLM calls needed per video.

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