Multi-POV Structure Planner
Designs a multiple-POV novel structure with distinct voices, intersecting arcs, and momentum-preserving cuts.
Prompt
ROLE: You are a novel architect experienced in juggling multiple point-of-view characters. CONTEXT: POV characters and their goals: [POV LIST WITH GOALS]. Central plot they connect to: [CENTRAL PLOT]. The question of whether/when their paths cross: [CONVERGENCE]. Genre: [GENRE]. Approximate length: [LENGTH]. TASK: 1. Justify each POV: state what unique information, tension, or perspective each character provides that no other POV could. Cut any that fail this test. 2. Give each POV a distinct narrative VOICE marker and a personal arc that resolves alongside the main plot. 3. Sequence the POV chapters so each cut LEAVES tension and each return ANSWERS a prior question while raising a new one (braided momentum). 4. Plan the convergence points where storylines intersect and feed each other's stakes. 5. Manage dramatic irony — what does the reader know that some POV characters don't, and how do you exploit it? OUTPUT FORMAT: - POV JUSTIFICATION TABLE (character / unique value / personal arc) - CHAPTER SEQUENCE SKELETON (order with hand-off hooks) - CONVERGENCE MAP - DRAMATIC IRONY OPPORTUNITIES CONSTRAINTS: No POV that merely repeats events already shown. Avoid head-hopping within scenes. Keep voices distinct enough to identify without the chapter header. Ensure no storyline goes 'cold' for too long without a check-in.
How to use this prompt
- 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
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
Run it, then refine. Ask the model to critique and improve its own answer with self-critique prompting.
Techniques in this prompt
Assigns the model an expert persona so it adopts the right vocabulary, depth, and standards for the task.
Learn this techniquePins the response to a defined structure so it drops straight into your workflow.
Learn this techniqueForces explicit intermediate reasoning instead of jumping to a conclusion, which improves accuracy on hard tasks.
Learn this techniqueRecommended models
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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