, etc. Use paragraphs
. Use WordPress block comment syntax? The instruction says write as plain HTML paragraphs and headings (e.g.,
…
). So we should include those comments. Probably each paragraph should be wrapped in those comments. But we can also just output plain HTML paragraphs; but they gave example. Safer to follow example: each paragraph:content
. Headings similarly: maybeHeading
. We’ll produce several sections: Introduction, Pre-Edit (Strategic), In the NLE (Execution), Final Polish (Quality Control), Human Touch Points (Comedic Timing, Contextual Awareness, Narrative Flow, Quality Control), Benefits, Conclusion. We need to embed facts from e-book: list bullet points? We can embed as sentences. We must ensure word count 450-500. Let’s draft then count. I’ll write content then count manually. Draft:Independent video editors juggle tight deadlines while trying to keep a creator’s voice authentic. AI can automate raw‑footage summarization and clip selection, turning hours of review into a 20‑minute task, but the final cut still needs a human eye.
Pre‑Edit (Strategic)
Start by feeding your raw footage into an AI summarization tool. The AI will generate a text summary, flag potential highlights, and produce a rough edit called an “Assembly_AI” sequence.
Create a dedicated sequence in your NLE named Assembly_AI and place the AI‑generated clips there. This assembly acts as a visual guide you can play through instantly.
While watching, note gaps in the story that the AI missed, awkward jumps, and where pacing feels off (a clip too long or short). Use the AI summary as the basis for chapter markers in the timeline.
In the NLE (Execution)
Drag the approved AI clips from Assembly_AI onto your main timeline. Keep the original AI suggestions on a separate track for reference.
For podcasts or talking‑head segments, let the AI flag main discussion points and remove ums/ahs, then fine‑tune the cuts manually.
Insert establishing shots (the bustling market crowd) and transitional B‑roll (a quick shot of train wheels moving) where the AI left gaps, ensuring visual continuity.
Build a selective B‑roll and A‑roll library: tag clips by theme, location, or emotion so you can pull the right material quickly during this stage.
Final Polish (Quality Control)
Play the timeline as an audience member. Does the story hold? Are there awkward jumps? Apply the human‑only skills that AI cannot replicate:
Comedic Timing: Hold a reaction shot a beat longer than the AI suggested to let the joke land.
Contextual Awareness: Recognize inside jokes, recurring segments, or the creator’s unique style that the AI overlooks.
Narrative Flow: Shape the story arc, emotional beats, and pacing that the audience expects.
Quality Control: Spot and reject clips with poor audio, awkward framing, or continuity errors the AI might have missed.
When a clip works perfectly, leave it as‑is; otherwise, replace it with a better take from your library.
Result
By combining AI’s speed with human judgment, editors reduce raw‑footage review from hours to minutes while preserving the creator’s voice and delivering polished highlights that keep viewers engaged.
For a comprehensive guide with detailed workflows, templates, and additional strategies, see my e-book: AI for Independent Video Editors (for YouTube Creators): How to Automate Raw Footage Summarization and Clip Selection for Highlights.
Now we need to count words. We’ll count words manually. Let’s copy the text and count. Title line: “Title: The Human-AI Workflow: From AI Suggestions to Final Cut Pro/A Premiere Timeline” Count words: Title:(1) The(2) Human-AI(3) Workflow:(4) From(5) AI(6) Suggestions(7) to(8) Final(9) Cut(10) Pro/A(11) Premiere(12) Timeline(13). So 13 words. Now we need to count words in the HTML content (excluding HTML tags? Usually word count includes visible text only. We’ll count words insideand headings etc., ignoring tags and comments. Let’s extract visible text. Paragraph 1: “Independent video editors juggle tight deadlines while trying to keep a creator’s voice authentic. AI can automate raw‑footage summarization and clip selection, turning hours of review into a 20‑minute task, but the final cut still needs a human eye.” Count words: Independent(1) video2 editors3 juggle4 tight5 deadlines6 while7 trying8 to9 keep10 a11 creator’s12 voice13 authentic14. AI15 can16 automate17 raw‑footage18 summarization19 and20 clip21 selection,22 turning23 hours24 of25 review26 into27 a28 20‑minute29 task,30 but31 the32 final33 cut34 still35 needs36 a37 human38 eye39. So 39 words. Heading 2: “Pre‑Edit (Strategic)” words: Pre‑Edit(1) (Strategic)2? Actually parentheses maybe not count as word. Let’s count: Pre‑Edit(1) Strategic(2). So 2. Paragraph after heading: “Start by feeding your raw footage into an AI summarization tool. The AI will generate a text summary, flag potential highlights, and produce a rough edit called an “Assembly_AI” sequence.” Count: Start1 by2 feeding3 your4 raw5 footage6 into7 an8 AI9 summarization10 tool.11 The12 AI13 will14 generate15 a16 text17 summary,18 flag19 potential20 highlights,21 and22 produce23 a24 rough25 edit26 called27 an28 “Assembly_AI”29 sequence30. 30 words. Next paragraph: “Create a dedicated sequence in your NLE named Assembly_AI and place the AI‑generated clips there. This assembly acts as a visual guide you can play through instantly.” Count: Create1 a2 dedicated3 sequence4 in5 your6 NLE7 named8 Assembly_AI9 and10 place11 the12 AI‑generated13 clips14 there.15 This16 assembly17 acts18 as19 a20 visual21 guide22 you23 can24 play25 through26 instantly27. 27 words. Next paragraph: “While watching, note gaps in the story that the AI missed, awkward jumps, and where pacing feels off (a clip too long or short). Use the AI summary as the basis for chapter markers in the timeline.” Count: While1 watching,2 note3 gaps4 in5 the6 story7 that8 the9 AI10 missed,11 awkward12 jumps,13 and14 where15 pacing16 feels17 off18 (a19 clip20 too21 long22 or23 short).24 Use25 the26 AI27 summary28 as29 the30 basis31 for32 chapter33 markers34 in35 the36 timeline37. 37 words. Heading: “In the NLE (Execution)” words: In1 the2 NLE3 (Execution)4 => 4? Actually “In”(1) “the”(2) “NLE”(3) “(Execution)” maybe counts as one word? We’ll count “Execution”(4). So 4. Paragraph: “Drag the approved AI clips from Assembly_AI onto your main timeline. Keep the original AI suggestions on a separate track for reference.” Count: Drag1 the2 approved3 AI4 clips5 from6 Assembly_AI7 onto8 your9 main10 timeline.11 Keep12 the13 original14 AI15 suggestions16 on17 a18 separate19 track20 for21 reference22. 22 words. Paragraph: “For podcasts or talking‑head segments, let the AI flag main discussion points and remove ums/ahs, then fine‑tune the cuts manually.” Count: For1 podcasts2 or3 talking‑head4 segments,5 let6 the7 AI8 flag9 main10 discussion11 points12 and13 remove14 ums/ahs,15 then16 fine‑tune17 the18 cuts19 manually20. 20 words. Paragraph: “Insert establishing shots (the bustling market crowd) and transitional B‑roll (a quick shot of train wheels moving) where the AI left gaps, ensuring visual continuity.” Count: Insert1 establishing2 shots3 (the4 bustling5 market6 crowd)7 and8 transitional9 B‑roll10 (a11 quick12 shot13 of14 train15 wheels16 moving)17 where18 the19 AI20 left21 gaps,22 ensuring23 visual24 continuity25. 25 words. Paragraph: “Build a selective B‑roll and A‑roll library: tag clips by theme, location, or emotion so you can pull the right material quickly during this stage.” Count: Build1 a2 selective3 B‑roll4 and5 A‑roll6 library:7 tag8 clips9 by10 theme,11 location,12 or13 emotion14 so15 you16 can17 pull18 the19 right20 material21 quickly22 during23 this24 stage25. 25 words. Heading: “Final Polish (Quality Control)” words: Final1 Polish2 (Quality3 Control)4 => 4. Paragraph: “Play the timeline as an audience member. Does the story hold? Are there awkward jumps? Apply the human‑only skills that AI cannot replicate:” Count: Play1 the2 timeline3 as4 an5 audience6 member.7 Does8 the9 story10 hold?11 Are12 there13 awkward14 jumps?15 Apply16 the17 human‑only18 skills19 that20 AI21 cannot22 replicate23. 23 words.