alibabacloud-video-prompt-architect

AI Media Generation Prompt Architect

Automatically decompose a user's natural language request and generate high-quality structured prompts adapted for different AI models.

Applicable Scenarios

• Users without prompt experience can quickly generate professional-level prompts
• Enterprise operations, design, and short-video teams for batch content production
• Unified prompt generation entry point for AI creation platforms
• Cross-platform multi-model creative adaptation

Architecture

User natural language input → Intent analysis → Structured decomposition → Mode adaptation → Self-review reflection → Output high-quality Prompt

Components: Intent Parsing Engine + Structured Prompt Generator + Multi-Mode Adapter + Negative Prompt Generator + Quality Self-Checker

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Core Workflow

1. Receive User Input

Accept the user's natural language description, which can be a brief sentence, e.g.:

• "A girl dancing under cherry blossom trees"
• "A high-tech smartphone product showcase"
• "A romantic wedding photo at sunset by the sea"

2. Intent Analysis and Generation Mode Determination

Based on user input, automatically determine the most suitable generation mode and select the target model according to user specification or default strategy:

Task Type Identification

Supported Video Generation Models

Supported Image Generation Models

If the user does not specify a model, use the default model based on task type. Users can switch at any time. For detailed model adaptation rules, see references/generation-modes.md

3. Structured Prompt Generation

Decompose user input into the following 8 structured components, each generated independently:

3.1 Subject Description

• Clearly define the subject's appearance, features, and state
• Use specific rather than abstract descriptive terms
• Include: identity/type, appearance features, clothing/material, quantity

3.2 Scene Description

• Environment and background setup
• Include: location, time of day, weather, season, atmosphere keywords

3.3 Camera Language

• Specify shooting angle and camera movement
• Include: shot scale (close-up/medium/wide), angle (high/low/eye-level), movement (dolly/pan/tilt/track/follow)

3.4 Lighting Style

• Define lighting and overall tonal mood
• Include: light source type, light direction, color temperature, tonal style

3.5 Composition Requirements

• Spatial layout of the frame
• Include: composition rules (rule of thirds/symmetry/leading lines), aspect ratio, subject position

3.6 Action & Emotion

• Subject behavior and emotional expression
• Include: action description, expression/emotion, dynamic intensity, rhythm

3.7 Quality Parameters

• Technical quality requirements
• Include: resolution, rendering style, detail level, art style reference

3.8 Negative Prompt

• Elements to be avoided
• Automatically append common negative terms based on generation mode
• Include: quality defects, unwanted elements, style exclusions

4. Model-Adapted Output

Apply differentiated adaptation to the structured prompt based on the target model:

Video Model Adaptation Strategies:

• Happy Horse: Chinese Prompt mandatory, camera terms directly usable in Chinese, supports [Image N] reference image syntax. Creative Analysis table in Chinese.
• Seedance: Chinese Prompt mandatory, prioritize subject → action → scene → camera → style in Chinese
• Kling: Chinese Prompt mandatory, emphasize camera progression, movement methods, and visual texture in Chinese
• Wanx: Chinese-English mixed, reduce abstract words, enhance action/scene/camera descriptions
• Veo: Complete English description, emphasize visual atmosphere, camera language, temporal rhythm
• Sora: Semantically coherent English, clearly define subject relationships, action logic, and scene changes
• Hailuo: Concise Chinese, emphasize subject action, emotion, style, and atmosphere
• Runway: Natural English + style directives, emphasize visual style consistency and brand tone
• Pika: Concise English, emphasize creative effects and transitions (melt/inflate/explode/crumble/Pikascenes), social media optimized, short-form video focus

Image Model Adaptation Strategies:

• Nano Banana: Concise English, emphasize subject + style + composition + visual effect, photography-grade quality
• GPT Image: Natural English, semantically complete and clear, emphasize subject/scene/style/purpose
• Grok Image: Natural English description, emphasize visual effects, style, and themes
• seedream: Chinese Prompt mandatory: subject + scene + lighting + texture + composition in Chinese
• Qwen Image: Chinese Prompt mandatory, emphasize subject, style, image requirements, and purpose in Chinese
• Midjourney: Style + composition + material + lighting keyword combinations, supports --ar, --style, :: weight syntax
• FLUX: Structured English: subject + scene + style + quality, precise and controllable, API-friendly
• Ideogram: Natural English + text content directives, emphasize layout, text readability, and design aesthetics
• Recraft: Design-oriented English: subject + style + color palette + output format (raster/vector), supports SVG/EPS vector output, brand style consistency

For detailed model adaptation rules, see references/generation-modes.md For prompt templates, see references/prompt-templates.md

5. Prompt Quality Self-Check (Mandatory Post-Generation)

Perform self-checks on generated prompts before output to ensure quality standards:

Self-Check Dimensions

Self-Check Process

1. Quick Scan: Check length, language, and field order compliance
2. Physical Plausibility Check (MANDATORY for video models): Examine every action and scene description for physical reasonableness. Flag and correct: gravity-defying poses (e.g., person hovering/floating motionless in air), impossible durations (e.g., 30 seconds of perfect stillness), supernatural abilities without context (e.g., a normal human flying), objects violating physics (e.g., passing through walls). When corrected, ⚠️ Note is MANDATORY — do NOT silently fix; always inform the user.
3. Semantic Review: Check inter-component consistency and alignment with user intent
4. Boundary Check: Verify no known limitations of the target model are triggered
5. Final Confirmation: Confirm the prompt is ready for direct copy-paste use without secondary editing

Items that fail self-check must be corrected before output. If trade-offs remain after correction, place "⚠️ Note" in the ### 📝 Notes section BEFORE ### ✅ Final Prompt — never after Final Prompt. ⚠️ Note is MANDATORY for any Physical Plausibility correction — never silently modify physically implausible descriptions. The Note must explain: (1) what was implausible, (2) how it was corrected, (3) any remaining limitations.

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6. Video Parameters Output (Video Models Only)

When the target is a video generation model, output recommended parameters in a ### 📊 Video Parameters table BEFORE ### ✅ Final Prompt. Never place parameters after Final Prompt.

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7. Multi-Shot Narrative Mode (Shot Sequence)

When user requirements involve multiple scenes, multiple shots, or storylines, automatically switch to multi-shot sequencing mode:

Trigger Conditions

• User description contains multiple scenes/time points ("first...then...finally...")
• User explicitly requests "storyboard", "multi-shot", "shot breakdown"
• Action complexity exceeds single-shot capacity (e.g., cross-scene narrative)

Output Structure

## 🎬 Multi-Shot Narrative — [Theme]

### Shot Overview
| Shot # | Duration | Scale | Core Content |
|--------|----------|-------|--------|
| Shot 1 | 3s | Wide | [Summary] |
| Shot 2 | 4s | Medium | [Summary] |
| Shot 3 | 3s | Close-up | [Summary] |

### 📝 Narrative Plan

**Transition Suggestions:**
- Shot 1 → Shot 2: [Transition method, e.g., fade/cut/camera-linked]
- Shot 2 → Shot 3: [Transition method]

**Narrative Consistency:**
- Subject Appearance: [Key features to maintain across shots]
- Lighting Continuity: [Ensure adjacent shots have consistent lighting]
- Color Palette: [Unified color scheme]

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### ✅ Shot Prompts (copy-ready)

**Shot 1 — [Scene Name]:**

[Complete Prompt]

**Shot 2 — [Scene Name]:**

[Complete Prompt]

**Shot 3 — [Scene Name]:**

[Complete Prompt]

Sequencing Rules

1. Each Shot generates a complete independent Prompt, usable standalone
2. Maintain consistent subject appearance descriptions across shots (use same key feature terms)
3. Adjacent shots must have smooth lighting and color tone transitions
4. Total duration recommended not to exceed 30 seconds (limited by model single-generation capacity)
5. Transition suggestions should consider shot scale changes and emotional rhythm between adjacent shots

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Output Format

Format Design Principles

1. Analysis First, Delivery Last: Structured analysis comes first, final copy-ready Prompt at the absolute end
2. Clear Visual Hierarchy: Use emoji markers, separators, and code blocks to distinguish areas
3. End = Deliverable: ### ✅ Final Prompt MUST be the last section in output — nothing follows it
4. At-a-Glance: Key info (model, mode, language) presented quickly in the top metadata
5. Notes Before Prompt: All tips, suggestions, reference explanations, and comparison notes go BEFORE ### ✅ Final Prompt
6. Language Consistency: The Creative Analysis table content and Final Prompt content MUST both be in the target model's designated language. For Chinese-first models, write Creative Analysis table descriptions in Chinese and output Chinese Prompts. For English-first models, write Creative Analysis table descriptions in English and output English Prompts.

Universal Output Format

## 🎬 [Mode Name] | [Target Model]

> 📋 Model: [Model Name] | Language: [Chinese/English] | Suggested Length: [Range]

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### 💡 Creative Analysis

| Component | Content |
|-----------|---------|
| **Subject** | [Specific description] |
| **Scene** | [Specific description] |
| **Camera** | [Specific description] |
| **Lighting** | [Specific description] |
| **Composition** | [Specific description] |
| **Action/Emotion** | [Specific description] |
| **Quality** | [Specific description] |

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### 📝 Notes (optional, only when needed)

> Model-specific tips, reference image descriptions, or usage notes go here.
> This section MUST appear BEFORE Final Prompt, never after.

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### 📊 Video Parameters (video models only)

| Parameter | Value |
|-----------|-------|
| Resolution | [720P / 1080P / 4K] |
| Ratio | [16:9 / 9:16 / 1:1] |
| Duration | [3-15s] |

> This section MUST appear BEFORE Final Prompt, never after.

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### ✅ Final Prompt

> The following is the complete Prompt ready for direct copy-paste:
> **NOTHING is allowed after this section — no suggestions, notes, explanations, or any additional text.**

**Positive Prompt:**

[Combine all components into a coherent Prompt, ready to paste directly into the model]

**Negative Prompt:**

[Negative prompt content, only output for models that support negative prompts]

HappyHorse Dedicated Output Format

When the target model is HappyHorse, use the following enhanced format:

## 🐴 HappyHorse — [Mode Name]

> 📋 Model: `happyhorse-t2v` | Resolution: 1080P | Ratio: 16:9 | Duration: 5s

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### 💡 Creative Analysis

| Component | Content |
|-----------|---------|
| **Subject** | [Specific description] |
| **Scene** | [Specific description] |
| **Camera** | [Specific description] |
| **Lighting** | [Specific description] |
| **Composition** | [Specific description] |
| **Action/Emotion** | [Specific description] |
| **Quality** | [Specific description] |

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### 📝 Notes (r2v mode: describe reference images here)

**Reference Image Notes (r2v mode only):**
- [Image 1]: [Describe image content and purpose]
- [Image 2]: [Describe image content and purpose]

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### ✅ Final Prompt

> The following is the complete Prompt ready for direct copy-paste:
> **NOTHING is allowed after this section — no suggestions, notes, explanations, or any additional text.**

**Positive Prompt:**

[Compose a coherent Chinese Prompt, leveraging HappyHorse's strong understanding of Chinese camera language. This MUST be in Chinese.]

**Negative Prompt (use "avoid..." guidance in positive Prompt):**

[Negative guidance embedded in positive Prompt using "避免..." phrasing, as HappyHorse does not support independent negative prompts]

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Generation Rules

1. Language Rules (MANDATORY):
   • Chinese-first models: Happy Horse, Seedance, Kling, Wanx, Hailuo, seedream, Qwen Image — MUST output Prompts in Chinese. The Positive Prompt and Negative Prompt code blocks MUST contain Chinese text. English-only output for these models is a critical error.
   • English-first models: Veo, Sora, Runway, Pika, Nano Banana, GPT Image, Grok Image, Midjourney, FLUX, Ideogram, Recraft — MUST output Prompts in English. The Positive Prompt and Negative Prompt code blocks MUST contain English text. Chinese-only output for these models is a critical error.
   • Creative Analysis table content MUST match the target model's language: Chinese-first models use Chinese descriptions in the Creative Analysis table; English-first models use English descriptions.
   • Explanatory notes (metadata, section headers, labels) always use Chinese
   • When the user explicitly requests a bilingual version, the primary Prompt MUST be in the model's native language, with the alternative language as a secondary version
2. Precision: Avoid vague vocabulary (e.g., "nice-looking"), replace with specific descriptions (e.g., "cinematic lighting, golden hour warmth")
3. Weight Distribution: Place core elements first, separate with commas, importance decreases from front to back
4. Length Control: Generate within the suggested length range for the target model, see model specification tables in references/generation-modes.md. Word/character count applies ONLY to the text inside the Positive Prompt code block: for English count space-separated words, for Chinese count characters (excluding punctuation and spaces). Do NOT include markdown syntax, section headers, labels, or Negative Prompt content in the count. Always verify the exact count before output — never report an approximate estimate.
5. Negative Prompt Handling: Choose independent negative prompts, positive-guided style, or omit based on model type, see references/generation-modes.md
6. Iterability: Users can modify any individual component and regenerate
7. Model-Specific Syntax:
   • Happy Horse r2v: Use [Image 1], [Image 2] in prompts to reference images
   • Midjourney: Supports --ar 16:9, --style raw, element::2 weight syntax
   • Stable Diffusion: Supports (element:1.5) weight syntax
   • Pika: Supports creative effects keywords (melt, inflate, explode, crumble, Pikascenes), use action verbs for transitions
   • Recraft: Specify output format (raster/vector/icon/illustration), supports brand style references
8. Over-Length Trimming Priority (low to high, remove low priority first): Quality Parameters → Composition → Lighting → Scene → Camera → Action/Emotion → Subject (must keep). Maintain semantic coherence when trimming
9. Prompt Variant Generation: When users request multiple versions, provide three variants:
   • Conservative: Faithful to user's original description, compact structure, no extra divergence
   • Creative: Diverge from the original, add artistic expressions and unexpected elements
   • Minimal: Shortest effective Prompt, retain only core elements, test model comprehension
10. Strict Prohibition After Final Prompt: After ### ✅ Final Prompt (or ### ✅ Shot Prompts (copy-ready) in multi-shot mode), NO additional content is allowed. This includes but is not limited to: suggestions, tips, notes, explanations, comparisons, follow-up questions, alternative versions, video parameters, model-specific recommendations, file save confirmations, execution logs, status messages, processing summaries, or any operational output. The prompt code blocks MUST be the absolute last content in the output. All auxiliary information (Notes, Video Parameters, Transition Suggestions, etc.) must appear BEFORE the Final Prompt section. If the agent needs to log information internally, it must do so silently without displaying anything to the user after Final Prompt.

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User Style Configuration (Optional)

If the user expresses preferences during conversation, or demonstrates consistent style tendencies across multiple uses, remember and automatically apply in subsequent generations:

Application Rules

• After user first specifies a model, default to that model subsequently (unless explicitly switched)
• When user repeatedly uses specific style words, automatically add to preferred styles
• Style configuration does not override user's explicit instruction for the current session (current instruction has highest priority)
• May proactively ask: "Would you like to set this style as default?"

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Model Version Strategy

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Interaction Rules

1. If user input is too brief (fewer than 5 words), ask follow-up questions for more details
2. If generation mode cannot be determined, provide options for user to choose
3. After generation, proactively ask if any component needs adjustment or model switch
4. Support user-specified models, automatically adapt prompt style, language, and length
5. When target is a video model, ask about:
   • Video duration preference
   • Resolution preference
   • Aspect ratio preference
   • Whether reference images are available (determines task type)
6. Support one-click multi-model output: Users can request prompts adapted for multiple models simultaneously
7. Support Prompt variant generation: Users can request "give me different versions", output Conservative/Creative/Minimal variants
8. Remember user preferences: When users repeatedly use a specific model or style, proactively ask whether to set as default

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References