Common LLM Workflow Patterns

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.

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Pattern 1: Content Triage & Classification

Automatically categorize and route incoming content

How It Works

Content arrives (email, ticket, form submission)

LLM analyzes and categorizes the content

Workflow routes to appropriate handler based on category

Use Cases

📧 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.

✨

Pattern 2: Content Enhancement & Refinement

Improve existing content automatically

How It Works

Raw content is created or received

LLM improves grammar, tone, clarity, or formatting

Enhanced content is saved or published

Use Cases

📝 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 clear

🔍

Pattern 3: Structured Data Extraction

Extract structured data from unstructured content

How It Works

Unstructured content arrives (email, PDF, chat)

LLM extracts specific fields in structured format (JSON)

Data is written to database, CRM, or spreadsheet

Use Cases

📋 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.

📊

Pattern 4: Summarization & Reporting

Condense large amounts of information

How It Works

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

Use Cases

📈 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.

✍️

Pattern 5: Dynamic Content Generation

Create new content based on templates or data

How It Works

Trigger event provides context or data

LLM generates custom content based on template and data

Content is published or sent to recipient

Use Cases

✉️ 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 conversational

Best Practices Across All Patterns

Always Include Examples

Provide 2-3 examples of desired output format in your prompts for more consistent results.

Use Structured Output Formats

Request JSON or other structured formats when you need to parse the LLM's response programmatically.

Add Validation Steps

Include a module after the LLM to validate output format and content before taking action.

Implement Error Handling

Always add fallback logic for API failures, unexpected responses, or rate limits.

Monitor and Iterate

Review workflow execution logs regularly and refine prompts based on actual performance.

Combining Patterns

The most powerful workflows combine multiple patterns. For example:

• Triage → Enhancement → Generation: Classify support tickets, improve the response draft, generate personalized reply
• Extraction → Summarization → Reporting: Extract data from invoices, summarize monthly spending, generate expense report
• Classification → Content Generation: Categorize leads by quality, generate personalized follow-up for each segment