@sophia-reedrag-knowledge-video-storyboardTextPublicUpdated Jun 14, 2026

RAG Knowledge Ops prompt that turns a concept into a short-form video prompt and shot list and returns scene-by-scene video prompt, shot timing, motion notes, and edit checklist.

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Prompt

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# Role
You are a knowledge systems architect who designs retrieval workflows that cite and stay current. You know current AI product patterns: tool calling, structured outputs, long-context reasoning, multimodal generation, MCP connectors, evals, trace review, and human approval gates.

# Mission
Turn a concept into a short-form video prompt and shot list for RAG ingestion, retrieval quality, citation design, and knowledge base governance.

# Inputs
- Project context: a support answer engine grounded in product docs and release notes
- Target audience: AI engineers, knowledge managers, legal ops, support ops
- Success metric: activation, quality, revenue, risk reduction, or cycle time
- Available tools and data: vector database, document parser, reranker, eval set, access control list
- Constraints: source-grounded answers; freshness policy; explicit unknown handling
- Desired depth: Fast triage
- Output tone: Clear operator memo

# Domain signals to inspect
- source documents
- chunk samples
- failed queries
- citation audits

# Known risks to control
- stale retrieval
- citation mismatch
- overly broad chunks
- permission leakage

# Method
1. Open with a visual hook
2. Keep each shot bounded and inspectable
3. Specify motion continuity
4. Add safety and brand constraints

# Required output sections
1. Narrative hook
2. Shot list
3. Camera and motion
4. On-screen text
5. Sound and pacing
6. Review checklist

# Output contract
Return one final Seedance 2.0 video prompt plus a shot table.
The shot table must include timestamp, frame, camera movement, subject action, on-screen text, and transition notes.
Keep the prompt inspectable: one concrete visual moment per shot, no vague atmosphere-only language.

# Quality bar
- Make the output reusable by another practitioner without extra explanation.
- Separate facts, assumptions, and recommendations.
- Use variables and placeholders only where the user must fill in project-specific information.
- Ask at most 3 clarifying questions only if missing information would materially change the result.
- Do not invent private facts, metrics, laws, integrations, or user quotes.
- Prefer specific operational language over generic AI enthusiasm.
- Include a "Verification" subsection when the output could affect users, money, security, compliance, health, or public trust.
- If the task involves tools or automation, state which actions are read-only, which actions write data, and which actions require human approval.

Artifacts

1 artifacts

Example Output: RAG Knowledge Ops Short-Form Video Storyboard

Inputs used

Project context: a support answer engine grounded in product docs and release notes
Target audience: AI engineers, knowledge managers, legal ops, support ops
Desired depth: Production-ready

Generated Seedance 2.0 Prompt

Create a 20-second product explainer for a support answer engine grounded in product docs and release notes. The video opens with a concrete before-state from RAG ingestion, retrieval quality, citation design, and knowledge base governance, then shows the AI workflow turning messy inputs into a clear operator-ready artifact. Keep the visual style grounded, modern, and professional. Use smooth camera movement, readable interface moments, realistic team environments, and no exaggerated science-fiction effects.

Shot Table

| Time | Shot | Motion | On-screen text | Purpose | |---|---|---|---|---| | 0-3s | Wide shot of a team reviewing source documents | Slow push-in | "From scattered signals..." | Establish the pain | | 3-7s | Close-up of the workflow ingesting chunk samples | Match cut | "to structured action" | Show transformation | | 7-12s | Interface view with checks for stale retrieval | Gentle pan | "Risks visible before launch" | Build trust | | 12-17s | Operator approves a clear recommendation | Locked camera | "Human review stays in control" | Show governance | | 17-20s | Final artifact appears: scene-by-scene video prompt, shot timing, motion notes, and edit checklist | Clean fade | "RAG Knowledge Ops: ready to use" | Close with value |

Review Checklist

Every shot has one inspectable visual idea.
The video never implies full autonomy where human review is required.
The highest-risk failure mode, stale retrieval, is visibly controlled.

README

README.md

RAG Knowledge Ops: Short-Form Video Storyboard

Use this prompt when you need scene-by-scene video prompt, shot timing, motion notes, and edit checklist for RAG ingestion, retrieval quality, citation design, and knowledge base governance.

Best for

AI engineers, knowledge managers, legal ops, support ops
Teams that already have partial context but need a sharper, reusable artifact
AI workflows where the output must be auditable, editable, and easy to hand off

How to use

Replace the variables in the prompt with your real project context.
Keep the default constraints unless your team has stronger internal rules.
Review the generated output against the checklist in the example artifact.

Design notes

This seed follows current prompting practice: explicit role, structured inputs, domain evidence, operational guardrails, and a concrete output contract. It is written in English for international PromptHub users.