Choosing the Right AI Node

1. Dynamic Email Generation Pipeline
Input: Stream of customer inquiries
Prompt: "Write a response to these customer queries about {topic}"
Context: Customer data, company guidelines, product details
Output: Customized responses for each customer

2. Data Transformation Flow
Input: Raw business metrics
Prompt: "Convert this raw data into a quarterly report highlighting key trends"
Context: Previous reports, reporting guidelines
Output: Formatted reports for each dataset

3. Content Localization Workflow
Input: Marketing content in English
Prompt: "Translate and adapt this content for {target_market}"
Context: Cultural guidelines, local preferences
Output: Market-specific content versions

1. Support Ticket Processing
Input: Stream of support tickets
Categories: 
- Bug Report: "Issues with existing features"
- Feature Request: "New functionality asks"
- Account: "Login, access, billing issues"
- Security: "Security concerns or breaches"
Output: Category + justification for each ticket
Next Steps: Route to appropriate team, set priorities

2. Content Moderation Pipeline
Input: User-generated content feed
Categories:
- Safe: "Appropriate content"
- Needs Review: "Potentially inappropriate"
- Blocked: "Violates guidelines"
Output: Category for each content piece
Next Steps: Automatic approval/blocking/review routing

3. Email Processing Workflow
Input: Incoming email stream
Categories:
- Sales Lead: "Potential customer inquiries"
- Support: "Existing customer issues"
- Partnership: "Business collaboration requests"
- Other: "General inquiries"
Output: Category per email
Next Steps: Route to appropriate department, trigger responses

1. Invoice Processing Pipeline
Input: Stream of invoices
Fields to Extract:
- Invoice Number
- Date
- Amount
- Company Name
- Due Date
Output: Structured data for each field
Next Steps: Update accounting system, trigger payments

2. Resume Processing Workflow
Input: Batch of resumes
Fields to Extract:
- Name
- Email
- Skills
- Experience (years)
- Education
Output: Structured candidate data
Next Steps: Match to job openings, schedule interviews

3. Product Review Analysis
Input: Customer reviews feed
Fields to Extract:
- Product Name
- Rating
- Pros
- Cons
- Feature Mentions
Output: Structured review data
Next Steps: Update product analytics, trigger alerts

1. Lead Management System
Input: Various lead sources (forms, emails, calls)
Sheet Structure:
| Date | Name | Email | Source | Interest | Status |
Output: Populated rows for each lead
Next Steps: Lead scoring, sales assignments

2. Inventory Tracking
Input: Product updates, stock alerts
Sheet Structure:
| SKU | Location | Quantity | Last Updated | Reorder Status |
Output: Updated inventory rows
Next Steps: Reorder triggers, status reports

3. Event Registration Processing
Input: Registration forms, email RSVPs
Sheet Structure:
| Event | Attendee | Email | Ticket Type | Special Needs |
Output: Attendee list rows
Next Steps: Send confirmations, plan logistics

1. News Digest Automation
Input: Stream of news articles
Output: Concise summaries
Next Steps: Newsletter generation, alert system

2. Meeting Notes Processing
Input: Transcription feeds
Output: Key points and action items
Next Steps: Task creation, update tracking

3. Research Report Analysis
Input: Technical documents
Output: Executive summaries
Next Steps: Knowledge base updates, notifications

1. Customer Service Evaluation
Input: Stream of support conversations
Criteria:
- Solution Quality (0-40): Effectiveness of resolution
- Communication (0-30): Clarity and professionalism
- Efficiency (0-30): Response time and conciseness
Output: Score + justification per conversation
Next Steps: Performance tracking, training recommendations

2. Content Quality Assessment
Input: Stream of articles/posts
Criteria:
- Research Quality (0-40): Depth and accuracy
- Writing Style (0-30): Clarity and engagement
- SEO Optimization (0-30): Keywords and structure
Output: Score + breakdown per piece
Next Steps: Publishing decisions, improvement requests

3. Product Review Analysis
Input: Customer reviews feed
Criteria:
- Product Satisfaction (0-40)
- Value for Money (0-30)
- Customer Service (0-30)
Output: Numerical scores + justification
Next Steps: Product improvements, service adjustments

1. Product Demo Processing
Input: Stream of product demonstration videos
Prompt: "Extract key features and specs demonstrated"
Output: Detailed feature lists per video
Next Steps: Update product docs, create timestamps

2. Training Video Analysis
Input: Educational content videos
Prompt: "List all steps and procedures shown"
Output: Step-by-step process documentation
Next Steps: Create guides, update knowledge base

3. Content Moderation Flow
Input: User-generated video content
Prompt: "Identify any inappropriate content or safety concerns"
Output: Content analysis and flag recommendations
Next Steps: Approval/rejection, creator notifications

4. Social Media Video Processing
Input: Marketing video content
Prompt: "Extract key messages and calls to action"
Output: Content highlights and engagement points
Next Steps: Generate descriptions, tag content

1. Document Processing Pipeline
Input: Stream of scanned documents
Prompt: "Extract all text and form fields"
Output: Extracted text and data
Next Steps: Database updates, verification workflows

2. Product Photo Analysis
Input: E-commerce product images
Prompt: "Describe product features and characteristics"
Output: Detailed product descriptions
Next Steps: Catalog updates, listing generation

3. Social Media Monitoring
Input: Brand-related image feed
Prompt: "Identify our products and how they're being used"
Output: Product usage analysis
Next Steps: Engagement tracking, trend analysis

4. Real Estate Listing Processing
Input: Property photo sets
Prompt: "List visible amenities and property features"
Output: Property feature descriptions
Next Steps: Listing updates, property comparisons

Ask AI Approach:
Prompt: "Categorize this ticket and extract priority level"
Problems:
- Need complex prompting for consistent format
- Requires parsing to extract category
- May need multiple runs for consistent results

Categorizer Node Approach:
Input: Same ticket
Output: Direct category + justification
Benefits:
- Consistent output format
- Single step process
- Ready for automation
- More reliable for high-volume processing

Ask AI Approach:
Prompt: "Extract name, email, and phone number from this text. Format as Name: [name], Email: [email], Phone: [phone]"
Problems:
- Need to specify exact output format
- Requires parsing structured text response
- Format might vary between runs
- May miss fields or include extra information

Extract Data Node Approach:
Input: Text + Field definitions (name, email, phone)
Output: Direct field values in consistent format
Benefits:
- Guaranteed field extraction
- No parsing needed
- Consistent missing field handling
- Ready for database insertion

Ask AI Approach:
Prompt: "Rate this content from 0-100 on clarity (0-40), accuracy (0-30), and style (0-30). Provide scores and explanation."
Problems:
- Need to parse numerical scores from text
- Scoring might be inconsistent
- May need to validate score ranges
- Complex prompt to maintain scoring criteria

Scorer Node Approach:
Input: Content + Scoring criteria
Output: Direct numerical score + justification
Benefits:
- Guaranteed numerical output
- Consistent scoring criteria
- Built-in score validation
- Ready for analytics/reporting