PlaybookPrompts

Generate a scene-by-scene outline for a short-form episode

Creative & Design storytellingpodcastvideooutlining

Podcast episodes, video essays, and branded mini-docs often stall because the overall idea is clear but the scene-level structure isn't. This prompt produces a workable scene list before you start writing or recording.

Prompt
You are a documentary and narrative podcast producer. I need a scene-by-scene outline for a short-form episode.

Topic or story: {{TOPIC_OR_STORY}}
Format (podcast, video essay, mini-doc, etc.): {{FORMAT}}
Target runtime in minutes: {{TARGET_RUNTIME}}
Tone (e.g., investigative, conversational, essayistic, warm): {{TONE}}

1. Write a one-paragraph editorial brief: the single question this episode answers and why a listener/viewer should care right now.
2. List 5–8 scenes in order. For each scene provide:
   a. A working title (5 words or fewer)
   b. What happens or what is argued
   c. The emotional or intellectual shift it creates in the audience
   d. Estimated runtime in seconds
3. Identify the episode's 'turn' — the moment where the audience's understanding of the topic changes. Place a marker in your scene list.
4. Write the first 3 sentences of the opening scene as a draft.
5. Flag any scene that requires external sources, archival material, or guest contributions that you may not have.

Note: This outline works best for episodes under 30 minutes. For longer formats, treat each major section as its own mini-outline and repeat this process per section.
Variables to fill in
  • {{TOPIC_OR_STORY}}
  • {{FORMAT}}
  • {{TARGET_RUNTIME}}
  • {{TONE}}

How to use this prompt

  1. Copy the prompt above (Copy button on the top-right).
  2. Replace each {{VAR}} with your own value. Variables: {{TOPIC_OR_STORY}}{{FORMAT}}{{TARGET_RUNTIME}}{{TONE}}.
  3. Paste it into one of the recommended tools below.
  4. Iterate: tighten constraints in the prompt if the output is generic.

Why this prompt is structured this way

The prompt is split into explicit steps because LLMs do better when the path is named, not implied. Each variable forces specificity at the input layer — vague inputs get vague outputs.

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