Primary Use Case: Generative Content Acceleration

Background Overview

Private domain operations require massive content: posters, short videos, copy, group scripts. AIGC can significantly improve output efficiency but needs integration with asset centers, approval processes, and brand guidelines. This primary use case focuses on AIGC content production, review, application, and effect iteration, creating a "AI-assisted operations" content factory.

Goals & Value

• Rapid Generation: Automatically generate posters, copy, scripts based on themes and tags.
• Brand Consistency: Embed brand guidelines, tone, and visual templates.
• Efficient Approval: AI pre-review + manual confirmation, shorten launch time.
• Effect Feedback: Content performance feeds back to models for continuous optimization.

Participating Roles

• Content Operations/Design: Initiate requirements, proofread, publish.
• Brand & Legal: Review compliance and brand consistency.
• Data Team: Analyze content effects, train models.
• IT/Platform Team: Integrate AIGC services and asset centers.
• Sales/Customer Service: Use generated content to interact with customers.

Primary Scenario User Story

As a content operations staff, I want AI to help generate high-quality assets and launch quickly, so that I can support high-frequency social activities.

Sub-scenario Details

Sub-scenario A: Requirement Input & Content Generation

• Roles & Triggers: Operations initiates content creation.
• Main Process:
  1. Fill in theme, target audience, tone, key information.
  2. Select templates (posters, short video scripts, group scripts).
  3. AIGC automatically generates content with multi-version comparison.
  4. Operations selects and fine-tunes.
• Success Criteria: Fast generation; content meets requirements; multi-version output possible.
• Exceptions & Risk Control: Generation failure alerts; sensitive word filtering; provide recall.
• Metric Suggestions: Generation time, hit rate, manual editing time.

Sub-scenario B: AI Pre-review & Manual Approval

• Roles & Triggers: Content output requires review.
• Main Process:
  1. AI performs preliminary review based on brand guidelines and compliance vocabulary.
  2. Mark risk items (sensitive words, prohibited elements), provide modification suggestions.
  3. Manual review confirmation, store in library after approval.
  4. Approval records and version control completed.
• Success Criteria: High review efficiency; accurate risk identification; traceable processes.
• Exceptions & Risk Control: Pre-review misjudgments can be feedback-optimized; high-risk content requires multi-person approval; tamper-proof logs.
• Metric Suggestions: Review time, risk hit rate, misjudgment rate.

Sub-scenario C: Content Publishing & Reuse

• Roles & Triggers: Content goes online or is distributed.
• Main Process:
  1. Push generated content to asset library with tags and usage suggestions.
  2. Sales/customer service can call with one tap and make personalized adjustments.
  3. System counts usage times and channel performance.
  4. Low-effect content automatically suggests optimization or removal.
• Success Criteria: Content is easy to use; high reuse rate; trackable performance.
• Exceptions & Risk Control: Usage permissions controlled; activities automatically removed after expiration; record usage logs.
• Metric Suggestions: Content usage rate, conversion rate, removal rate.

Sub-scenario D: Effect Feedback & Model Optimization

• Roles & Triggers: Continuously improve content performance.
• Main Process:
  1. Collect data on impressions, clicks, conversions, interactions, etc.
  2. Evaluate effect differences across content and versions.
  3. Feed performance data back to AIGC models to optimize prompts and templates.
  4. Output effect reports and optimization suggestions.
• Success Criteria: Feedback closed loop; models continuously evolve; strategies are executable.
• Exceptions & Risk Control: Data missing alerts; automatic downgrade for extremely poor effects; retain historical version comparisons.
• Metric Suggestions: Content ROI, model iteration cycle, effect improvement amplitude.

Scenario-level Test Case Examples

Test Preparation: Integrate AIGC services, brand guidelines, approval processes, asset library, effect analysis reports.

Test Case A-1: Poster Generation & Approval (Positive)

• Prerequisites: Input theme "Spring Product Launch," templates configured.
• Steps:
  1. Generate poster and submit for approval.
  2. View pre-review and manual audit.
• Expected Results:
  • AI generates multiple poster versions with recommended copy.
  • Pre-review marks brand logo position as correct, no sensitive words.
  • After manual approval, poster stored in library, downloadable for use.

Test Case B-1: Sensitive Content Interception (Negative)

• Prerequisites: Intentionally input prohibited words.
• Steps:
  1. Generate content and submit.
  2. Check review results.
• Expected Results:
  • AI pre-review identifies sensitive words and blocks submission.
  • Provide modification suggestions or alternative words.
  • Review logs record this interception for compliance inspection.

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