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Building My First AI-Powered n8n Workflow

5 min read

Building My First AI-Powered n8n Workflow: A Learning Journey

What I Accomplished Today

Today was an exciting dive into n8n and AI workflow automation! I successfully set up my first AI-powered workflow using Groq’s LLM API, learned how to handle common issues, and created a practical automation for processing and formatting AI-generated content.

The Journey: From Setup to Success

1. Initial Goal: Setting Up Grok (xAI)

I started with the intention to integrate xAI’s Grok model into n8n for AI-powered workflows. However, I quickly ran into a common issue:

The Challenge:

  • When trying to save xAI credentials in n8n, I got a "Couldn't connect with these settings - Forbidden" error
  • The credential test kept failing even with a valid API key

The Solution: Since xAI is OpenAI-compatible, I learned to use OpenAI credentials instead:

  • Created OpenAI credentials in n8n
  • Set the Base URL to https://api.x.ai/v1
  • Used my xAI API key
  • This bypassed the connection test issue entirely!

2. Pivot: Discovering Groq

After researching alternatives, I decided to use Groq instead, which offers:

  • Incredibly fast inference speeds
  • Easy setup with n8n
  • Generous free tier for testing
  • OpenAI-compatible API

Groq Rate Limits (Free Tier):

  • 30 requests per minute (RPM)
  • 6,000 - 30,000 tokens per minute (TPM) depending on model
  • Up to 500,000 requests per day for some models

This was perfect for my learning and experimentation needs!

3. The Output Formatting Challenge

Once I had Groq running, I hit another roadblock:

The Problem: The AI’s output was filled with \n escape sequences instead of proper line breaks, making the markdown output unreadable and unusable for copy-paste.

Example of the messy output:

**Summary**  \n\n- **Point 1**  \n  - Detail  \n\n- **Point 2**  \n

The Solution - Code Node to Clean Output: I learned to add a Code node after the AI node to properly format the output:

// Get the output with \n characters
const output = $input.first().json.output;
 
// Replace literal \n with actual newlines
const cleanMarkdown = output
  .replace(/\\n/g, '\n')    // Convert \n to actual newlines
  .replace(/  \n/g, '\n');  // Remove trailing spaces
 
return { 
  json: { 
    content: cleanMarkdown
  } 
};

This transformed the messy output into beautiful, properly formatted markdown!

4. Alternative Solutions Explored

I also learned about several other approaches to formatting AI output in n8n:

  1. Structured Output Parser - Forces the LLM to return properly formatted JSON
  2. Prompt Engineering - Instructing the AI in the system prompt to avoid escape sequences
  3. HTML Conversion - Using the HTML node for rich formatting
  4. Markdown Node - Converting markdown to HTML when needed

Key Lessons Learned

About n8n:

  • Credential flexibility: Many APIs are OpenAI-compatible, allowing creative workarounds
  • Code nodes are powerful: They’re essential for data transformation and cleanup
  • Error messages aren’t always blockers: Sometimes you can ignore connection test failures
  • Multiple paths to success: n8n offers many ways to solve the same problem

About AI APIs:

  • Rate limits matter: Understanding TPM, RPM, TPD is crucial for production workflows
  • Free tiers are generous: Perfect for learning and prototyping
  • OpenAI compatibility is widespread: Many providers follow OpenAI’s API structure
  • Output formatting varies: Always plan for data cleanup steps

About Workflow Design:

  • Debug nodes are your friend: Adding nodes to inspect data structure saves time
  • Test incrementally: Build and test one node at a time
  • Plan for error handling: Rate limits and API issues will happen
  • Document as you go: Future you will thank present you

My Working Workflow

Here’s the final workflow I built today:

[Manual Trigger with Input]
    ↓
[Groq Chat Model / AI Agent]
    ↓
[Code Node - Format Output]
    ↓
[Write to File / Display Output]

What it does:

  1. Takes a URL or text input
  2. Processes it with Groq’s LLM (Llama 3.3 70B)
  3. Generates a formatted summary
  4. Cleans up the markdown formatting
  5. Outputs a properly formatted document ready for Obsidian

Practical Example: Azure DDoS Attack Summary

I tested the workflow by summarizing a technical article about a massive 15.72 Tbps DDoS attack on Microsoft Azure. The AI:

  • Extracted key information
  • Structured it with proper markdown formatting
  • Highlighted important technical details
  • Made it ready for my knowledge vault

The result was a clean, well-organized summary that I could immediately use!

Resources Created

During this session, I created:

  1. Setup Grok in n8n.md - Comprehensive guide covering:

    • xAI/Grok credential setup (with troubleshooting)
    • OpenAI-compatible workaround
    • Groq alternative
    • Rate limits and best practices
    • Example workflows

  2. Azure DDoS Attack Summary.md - Example output demonstrating:

    • Proper markdown formatting
    • Clean, readable structure
    • Ready-to-use format for Obsidian

  3. This learning log - Documenting the entire journey

Next Steps

Now that I have a working foundation, I want to explore:

  1. Advanced AI Agents - Using tools and function calling
  2. RAG (Retrieval Augmented Generation) - Connecting to vector databases
  3. Automated workflows - Setting up scheduled summaries
  4. Obsidian integration - Direct saving to my vault via API
  5. Multi-step processing - Chaining multiple AI operations
  6. Error handling - Proper retry logic and fallbacks

Reflections

This session was a perfect example of the learning process:

  • Started with one goal (Grok setup)
  • Hit obstacles (credential errors)
  • Found workarounds (OpenAI compatibility)
  • Discovered better alternatives (Groq)
  • Solved new problems (output formatting)
  • Built something useful (working workflow)

The key was staying flexible, reading documentation carefully, and iterating through solutions until finding what works.

Tips for Others Starting with n8n + AI

  1. Start simple - Don’t try to build everything at once
  2. Use the debug approach - Add Code nodes to inspect data structure
  3. Read error messages carefully - They’re more helpful than they seem
  4. Check rate limits early - Avoid surprises in production
  5. Document your workflows - Your future self will thank you
  6. Join communities - The n8n forum and Discord are incredibly helpful
  7. Experiment freely - The free tiers are generous enough for learning

Status: ✅ Successfully set up and tested
Confidence Level: Ready to build more complex workflows
Time Invested: ~2 hours
Value Gained: Enormous - now have a reusable AI workflow foundation!

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