You’ve dumped four hours of raw footage from a gaming session into your project folder. The creator needs a highlight reel by tomorrow. AI can scan and suggest clips, but you know the final cut needs more than algorithms—it needs timing, inside jokes, and narrative shape. The trick isn’t to fight AI or blindly trust it; it’s to build a repeatable human-in-the-loop workflow that turns raw automation into a polished timeline in under twenty minutes.
The Core Principle: Use AI for Drafting, Not Deciding
The biggest mistake editors make is treating AI suggestions as a final cut. Instead, treat them as a rough scaffold that you will evaluate with editorial judgment. The key is to create a dedicated sequence called “Assembly_AI” —a timeline built from the AI’s raw selects, transcript-based picks, and filler-word removals (using a tool like Descript to generate initial transcript-based highlights and clean up ums/ahs). This sequence is your diagnostic starting point, not your delivery.
Mini-scenario: For a travel vlog, the AI picks a ten-second establishing shot of a bustling market. But during your watch-through, you spot that the creator’s reaction shot—a friend laughing at a map confusion—happens a beat later. You swap the AI pick for the reaction, preserving the comedic timing and inside-joke context the automation missed.
Three High-Level Steps to Operationalise the Workflow
Step 1: Pre-Edit (Strategic) — Let AI Build Your Draft
Feed your raw footage into an AI tool that summarises scenes, flags main discussion points (for podcasts, it can also remove ums/ahs), and proposes a rough sequence of highlights. Export that output into your NLE as a new sequence named “Assembly_AI.” Resist the urge to tweak anything yet—you want a raw, unbiased draft.
Step 2: In the NLE (Execution) — The Pure Watch-Through
Play the “Assembly_AI” sequence from start to finish as an audience member. Don’t edit—just watch. You will instantly see:
- Gaps in the story where narrative flow breaks (e.g., a missing transitional shot of train wheels moving between locations).
- Pacing issues—clips that are too long or too short for emotional beats.
- AI suggestions that work and can stay as-is (keep those, they save time).
Mark the dead spots and decide where to insert reaction shots, establishing shots (the bustling market crowd), or contextual B-roll you know the creator’s audience expects.
Step 3: Final Polish (Quality Control) — Chapter Markers & Continuity
Use the AI-generated summary as the basis for chapter markers in the timeline—this gives structure quickly. Then perform a quality-control pass: reject clips with poor audio, awkward framing, or continuity errors the AI might have missed. Trim reaction holds for comedic timing, and ensure the story arc lands. This step—once a multi-hour manual assembly—now takes about 20 minutes because the heavy lifting is already scaffolded.
Conclusion
AI automation excels at summarisation and rough clip selection, but it cannot read inside jokes, gauge pacing, or catch audio glitches. Your editorial intuition is irreplaceable. By creating a dedicated “Assembly_AI” sequence, doing a pure watch-through, and layering in quality control, you turn AI suggestions into a timeline that feels human—fast. The result: highlights that honour the creator’s style, delivered on schedule.
