How front-loading local analysis cuts AI costs in video clipping pipelines

A developer built a pipeline to automatically extract short clips from long video recordings, finding that feeding a full 90-minute transcript to an AI model is both costly and unreliable due to context degradation. The solution front-loads most processing locally — including transcription, phrase-level packing, scene detection, and silence removal — before any paid AI call is made. Only one step, selecting candidate segments by topic, uses a language model, keeping costs minimal. A key insight is compressing word-level transcripts into phrase-level markdown so the model receives a concise, readable input rather than noisy raw data. The pipeline is also designed for iteration, with disk caching and serialised decision checkpoints so re-runs after tweaks cost nothing.