Proven patterns for integrating LLMs into your automation workflows
After building hundreds of AI-powered workflows, certain patterns emerge as particularly effective. These patterns solve common business problems and can be adapted to your specific needs.
Automatically categorize and route incoming content
Content arrives (email, ticket, form submission)
LLM analyzes and categorizes the content
Workflow routes to appropriate handler based on category
📧 Email Triage
Route support emails to the right team
🎫 Ticket Classification
Auto-assign priority and department
📄 Document Sorting
Organize incoming documents by type
💬 Social Media Monitoring
Categorize mentions and sentiment
Example Prompt:
Analyze this customer email and classify it into ONE of these categories:
- URGENT: Requires immediate attention
- SALES: Sales inquiry or product question
- SUPPORT: Technical support needed
- BILLING: Payment or invoice question
- FEEDBACK: General feedback or suggestion
Email: {{email_content}}
Respond with ONLY the category name.Improve existing content automatically
Raw content is created or received
LLM improves grammar, tone, clarity, or formatting
Enhanced content is saved or published
📝 Meeting Notes
Convert rough notes to polished summaries
📱 Social Posts
Optimize posts for each platform
✉️ Email Responses
Improve tone and professionalism
📄 Documentation
Generate clear, consistent docs
Example Prompt:
Improve this draft email for professionalism and clarity while maintaining the core message:
Draft: {{draft_email}}
Requirements:
- Keep the tone friendly but professional
- Fix any grammar or spelling errors
- Make it concise (under 200 words)
- Ensure the call-to-action is clearExtract structured data from unstructured content
Unstructured content arrives (email, PDF, chat)
LLM extracts specific fields in structured format (JSON)
Data is written to database, CRM, or spreadsheet
📋 Invoice Processing
Extract amounts, dates, and vendors
📝 Resume Parsing
Pull skills, experience, education
🏢 Lead Capture
Extract contact info from emails
📊 Survey Analysis
Structure free-text responses
Example Prompt:
Extract the following information from this lead email and return as JSON:
Email: {{email_content}}
Extract:
{
"company_name": "",
"contact_name": "",
"email": "",
"phone": "",
"industry": "",
"company_size": "",
"pain_points": [],
"budget_mentioned": true/false
}
Return ONLY valid JSON, no additional text.Condense large amounts of information
Collect data from multiple sources or time period
LLM identifies key insights and creates summary
Report is delivered via email, Slack, or saved to docs
📈 Daily Digests
Summarize daily activity across tools
📞 Call Transcriptions
Generate meeting action items
📰 News Monitoring
Weekly industry news roundups
💬 Community Insights
Aggregate social media feedback
Example Prompt:
Analyze these customer support tickets from the past 24 hours and create a summary:
Tickets: {{tickets_list}}
Provide:
1. Total tickets and average response time
2. Top 3 most common issues
3. Any urgent issues requiring immediate attention
4. Trends compared to last week
5. Recommendations for improvement
Keep the summary under 300 words.Create new content based on templates or data
Trigger event provides context or data
LLM generates custom content based on template and data
Content is published or sent to recipient
✉️ Personalized Emails
Custom outreach based on recipient data
📱 Social Content
Generate posts from blog articles
📄 Reports
Auto-generate weekly reports
🎯 Ad Copy
Create ad variations for testing
Example Prompt:
Generate a personalized cold outreach email using this prospect data:
Company: {{company_name}}
Industry: {{industry}}
Recent News: {{recent_news}}
Pain Point Identified: {{pain_point}}
Guidelines:
- Start with a relevant insight about their company or industry
- Connect their pain point to our solution
- Include a soft call-to-action for a brief call
- Keep it under 150 words
- Tone: Professional but conversationalProvide 2-3 examples of desired output format in your prompts for more consistent results.
Request JSON or other structured formats when you need to parse the LLM's response programmatically.
Include a module after the LLM to validate output format and content before taking action.
Always add fallback logic for API failures, unexpected responses, or rate limits.
Review workflow execution logs regularly and refine prompts based on actual performance.
The most powerful workflows combine multiple patterns. For example: