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YouTube Script — Built for Retention

Hook → reframe → 3 acts → payoff. Designed to hold 60%+ retention.

Role-BasedConstraintsChain-of-Thought

Prompt

**Role:** Top-decile YouTube editor who has analyzed 1,000+ retention graphs. You know exactly where viewers drop and why — and you've shipped 80+ videos that hold past 60% retention.

**Context:** Topic: [specific angle, not "video about X"]. Audience: [channel persona — what they already know, what they don't, what they care about]. Target length: [N minutes]. Differentiator: [the take only YOU can make on this topic].

**Task:** Write the full script with timecodes and b-roll cues.

1. First 8 seconds: a specific image or concrete claim that earns the scroll. NOT "Today we're going to talk about X." Show, don't preview.
2. Reframe (by 0:30): challenge the obvious answer. "Most people think X. Here's why that's not actually the problem."
3. Act 1 (0:30 - target/3): establish the real problem. End with a mini-payoff — a takeaway, a result, a number.
4. Act 2 (target/3 - 2target/3): the meat — the framework, the data, the story. End with another mini-payoff.
5. Act 3 (2target/3 - end): the synthesis or counter-intuitive insight.
6. Final payoff (last 30 seconds): tie back to the hook image. Earn the close.
7. Pattern interrupts every 20-30 sec: cuts, b-roll cues, callouts. Mark them with [b-roll: ...] in the script.

**Constraints:**
- No "Hey guys / Hi everyone"
- No "Don't forget to like and subscribe"
- No recap of previous video
- Word count = target length × ~150 words per minute

**Output format:** Timecoded script · b-roll cues in [brackets] · ≤target length × 150 words.

How to use this prompt

  1. 1

    Copy the prompt above and paste it into ChatGPT, Claude, or Gemini — or open it in the visual Studio to edit each part on a canvas and run it with your own key.

  2. 2

    Replace any bracketed placeholders with your specifics. The more concrete your context and constraints, the sharper the result — see the 5-part prompt structure.

  3. 3

    Run it, then refine. Ask the model to critique and improve its own answer with self-critique prompting.

Techniques in this prompt

Role-Based

Assigns the model an expert persona so it adopts the right vocabulary, depth, and standards for the task.

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Constraints

Sets the rules and boundaries — tone, length, what to avoid — that keep the output on-target.

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Chain-of-Thought

Asks the model to reason step by step before answering — ideal for multi-step, logical, or analytical tasks.

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Build on this prompt

Open it in the visual Studio to wire it into a full workflow with your own API key — or learn the craft behind prompts like this.

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