💻 4 Hidden N8N Errors That Waste Your Time And Quick Fixes To Solve Them

Introduction

Have you ever spent days, or even weeks, trying to figure out why your n8n automation workflow isn't working? You are not alone. In the world of automation, there are repeating patterns, and experience shows that most people make the same four basic mistakes when starting their journey.

These are not just small hiccups that can be fixed in five minutes. These are the types of errors that can leave you stuck for days, weeks, or even months. The good news? Once you know what to look for, each of these problems can be fixed in just a few minutes.

In this guide, we will walk through each common mistake in detail, helping you avoid frustration and build automation workflows that run smoothly from the start. Whether you're new to n8n or struggling with workflows that constantly fail for unknown reasons, this guide will save you countless hours of debugging and help you build more reliable automation systems.

Mistake #1: Not Using ChatGPT As A Data Extraction Tool

The Problem

One of the most common challenges in automation is dealing with messy, unstructured data. Imagine you receive potential customers via email, and each email contains information like this:

"Hello, my name is Nguyen Van An and my email is [email protected]. I am interested in your services and would like to know more about the pricing."

This is just one large block of text, but what you really need is organized data in Google Sheets with separate columns for first name, last name, email, and the message content. The question is: how do you automatically extract this information and arrange it correctly?

The Solution: ChatGPT In The Role Of A Data Parser

The secret lies in using ChatGPT not just for conversations, but also as a powerful tool to convert unstructured text into structured JSON data. Here is the exact way to set it up:

Step 1: Set Up Your ChatGPT Node

In your n8n workflow, add a ChatGPT node. You will need to configure it with three specific types of messages:

System Message (Context):

You are an intelligent bot specializing in extracting information from text such as first name, last name, email, and message.

This tells ChatGPT what its role is and what kind of information you want it to extract.

User Message (Input):

This is where you will input your unstructured text - the messy email or document containing the information you want to extract.

Assistant Message (Output Format):

This is where you define exactly how you want the data to be structured. You will provide a JSON template that looks like this:

JSON

{
  "first_name": "",
  "last_name": "",
  "email": "",
  "message": ""
}

Step 2: Create Your JSON Template

If you are not sure how to create the JSON structure, here is a simple trick. Go to ChatGPT and ask:

"Please create JSON data for me with the following variables: first name, last name, email, and phone number."

If it returns multiple people in the response, follow up with:

[
  {
    "first_name": "Alice",
    "last_name": "Nguyen",
    "email": "[email protected]",
    "phone_number": "+841234567890"
  },
  {
    "first_name": "David",
    "last_name": "Tran",
    "email": "[email protected]",
    "phone_number": "+849876543210"
  },
  {
    "first_name": "Linh",
    "last_name": "Pham",
    "email": "[email protected]",
    "phone_number": "+847654321098"
  }
]

"I only want one person and don't put anything in the value section."

This will give you a clean template that you can copy and paste into the assistant message in n8n.

Step 3: Enable JSON Output

In your ChatGPT node settings, make sure to turn on "Output content as JSON". This is very important as it tells the system to expect and parse JSON data instead of plain text.

Step 4: Map the Data

Once the ChatGPT node processes the text, it will output clean JSON data with separate fields for first name, last name, email, and message. You can then drag and drop these fields into the Google Sheets node or any other destination.

Why This Method Is More Effective Than Regular Expressions

Many people try to use complex regular expressions to extract data from text, but ChatGPT is much more flexible and intelligent. It can understand context, handle variations in how people write, and extract information even when the format is not perfectly consistent.

Mistake #2: Not Knowing How to Track Changes in Google Sheets

The Problem

Let's say you have successfully extracted potential customer information into a Google Sheet. Now you want to create an interactive system where you can check a box next to a customer's name and automatically send them an email. How do you make n8n "listen" for changes in your Google Sheet?

Many people get stuck here because they don't know how to connect Google Sheets changes to their n8n workflows.

The Solution: Webhooks + Google Apps Script

The answer involves creating a connection between Google Sheets and n8n by using a webhook and a small piece of Google Apps Script code. Don't worry - even if you are not technically savvy, this is much easier than it looks.

What Is A Webhook?

Before we start, let's clarify what a Webhook is. Imagine a webhook as a "special phone number" for your workflow. Instead of your workflow having to constantly ask Google Sheets, "Is there anything new? Is there anything new?", you give Google Sheets this phone number. When something happens (like a cell being edited), Google Sheets will proactively "call" that number and send the details of the change. This is much more efficient and instantaneous.

Step 1: Set Up Your Webhook in n8n

First, create a new workflow in n8n that starts with a Webhook node. Copy the webhook's URL - you will need it in the next step.

Step 2: Add the Apps Script Code

In your Google Sheet, go to Extensions → Apps Script. This action will open a code editor. Don't panic - you don't need to write any code from scratch.

This is the code you need to paste (replace YOUR_WEBHOOK_URL with the actual URL from step 1):

JavaScript

function onEdit(e) {
  var range = e.range;
  var sheet = e.source.getActiveSheet();
  var data = {
    'range': range.getA1Notation(),
    'value': e.value,
    'row': range.getRow(),
    'column': range.getColumn(),
    'sheet': sheet.getName()
  };
 
  var options = {
    'method': 'POST',
    'headers': {
      'Content-Type': 'application/json',
    },
    'payload': JSON.stringify(data)
  };
 
  UrlFetchApp.fetch('YOUR_WEBHOOK_URL', options);
}

Step 3: Set Up the Trigger

After pasting the code, you need to create a trigger that will run this code whenever someone makes a change to the sheet.

1. Click on the trigger icon (it looks like a clock).

2. Click "Add Trigger".

3. Choose "onEdit" as the function to run.

4. Set the event type to "On edit".

5. Save the trigger.

You may receive a warning that the app is not verified - just click "Advanced" and then "Go to [Project Name] (unsafe)" to proceed.

Step 4: Add Filtering Logic

Back in your n8n workflow, add a Filter node after the webhook. This filter will check two conditions:

1. Did the change happen in the specific column you care about (e.g., column E for checkboxes)?

2. Is the new value "true" (meaning the checkbox was checked)?

Only when both conditions are met will the workflow continue to the next step.

Step 5: Add Your Action

After the filter, you can add any action you want - send an email, create a task, update another system, etc.

Mistake #3: Being Afraid of HTTP Requests

The Problem

Many people see the HTTP Request node in n8n and immediately feel overwhelmed. They think it's too technical or complicated. But the truth is: every integration you see in n8n (Google Sheets, Slack, Telegram, etc.) is actually just a pre-packaged HTTP request.

When you want to connect to a service that doesn't have a pre-built node, you need to create your own HTTP request.

The Solution: Use API Documentation + Import Curl

Expansion: What is an API?

Let's decode this. Think of an API (Application Programming Interface) as a waiter in a restaurant. You (your n8n workflow) don't need to go into the kitchen (the other service's system) to get food (data). Instead, you give the waiter (the API) a specific request from the menu (the API documentation), and the waiter will bring back exactly what you requested. The HTTP request is how you "talk" to that waiter.

Step 1: Find the API Documentation

Let's say you want to integrate with ElevenLabs (an AI voice generation service). Here is how to find what you need:

1. Go to Google and search for "ElevenLabs API documentation".

2. Look for links that say "API docs" "API reference" or "Developer docs."

3. Navigate to the specific feature you want to use (like "text to speech") → Create speech

4. Copy

Step 2: Find the Curl Example

On the API documentation page, look for something called a "curl request" or "curl example."

A curl request looks something like this:

Bash

curl -X POST 'https://api.elevenlabs.io/v1/text-to-speech/voice-id' \
  -H 'Content-Type: application/json' \
  -d '{"text": "Hello world"}'

Step 3: Import Curl into n8n

This is the magic: you don't need to understand what all this code means. Just copy the entire curl request and go back to n8n.

1. Add an HTTP Request node to your workflow.

2. Find the "Import Curl" option.

3. Paste the entire curl request you copied.

4. Click "Import".

n8n will automatically fill in all the settings for you - the URL, method, headers, and body data.

Step 4: Add Authentication

The one thing that is usually not included in the curl example is authentication (your API key). You will need to add this manually in the "Authentication" or "Headers" section of the node, according to the API documentation's instructions.

Mistake #4: Not Understanding JSON Data Types

The Silent Killer

This is the mistake that causes the most frustration because it often does not give clear error messages. Your workflow may seem like it should work, but it just... doesn't work.

The problem is often related to JSON data types - the different ways data can be formatted and stored.

Understanding Data Types

In JSON (and in n8n), there are five main types of data:

{
  "name": "Alice Johnson", // String
  "age": 34,              // Number
  "isMember": true,       // Boolean
  "contact": {            // Object
    "email": "[email protected]",
    "phone": "123-456-7890"
  },
  "interests": ["AI", "automation", "cycling"] // Array
}

1. String (Text): Regular text. In n8n, you will see an "A" icon.

   • Examples: "Nguyen Van An", "[email protected]"

2. Number: Numeric data that you can perform math operations on. The icon is "#".

   • Examples: 25, 100.50

3. Boolean: True/false data. There are only two possible values: true or false (not the strings "true" or "false"). The icon is an on/off switch.

4. Object: Like a "folder" or a "contact card" that contains other pieces of data. It is marked with curly braces { }.

5. Array: A list of items, like a "shopping list". It is marked with square brackets [ ].

Why Data Types Matter

Different data types behave completely differently, even when they look the same.

• Math vs Text:

  • Number 2 + Number 2 = 4

  • Text "2" + Text "2" = "22"

• Boolean vs Text:

  • Boolean true ≠ Text "true" (they are completely different!)

Common Problems and Solutions

Let's say you have a filter that checks if someone is verified: "verified equals true".

If "verified" is stored as the text "true" instead of the boolean true, this condition will always fail.

The Solution: Look at the data type icons in n8n. If you see an "A" icon (text) where you expected a boolean icon, you need to convert the data type. Most nodes (like the Filter) have a "Convert Types" option. Turn it on, and n8n will automatically try to match the data types for you.

The Best Methods For Building Sustainable Workflows

1. Plan Before You Build: Spend a few minutes thinking about your data flow from start to finish before dragging and dropping any nodes.

2. Start Simple, Then Add Complexity: Don't try to build a complex workflow all at once. Start with a simple, working version, then gradually add more features.

3. Use Descriptive Names: Give your nodes clear names like "Extract Customer Info from Email" or "Check if User is Verified."

4. Always Test with Real Data: Use the actual, messy data you will encounter in your business. This will help you find data type issues and other problems before they break your automation.

5. Add Notes to Your Workflows: Add notes to your workflows to explain what each part does. Your future self will thank you when you need to modify something six months later.

Conclusion

These four mistakes - not using ChatGPT for data extraction, not knowing how to track changes in Google Sheets, being afraid of HTTP requests, and not understanding JSON data types - are the cause of most of the frustration people encounter when starting with n8n. The good news is that once you know what to look for, each of these problems can be solved quickly. More importantly, understanding these concepts will help you become a much more effective automation builder.

Remember, automation is supposed to save you time, not make you spend weeks on debugging. By avoiding these common mistakes, you can build reliable, consistently working workflows that truly deliver on the promise of automation: giving you back your time to focus on what really matters.

If you are interested in other topics and how AI is transforming different aspects of our lives, or even in making money using AI with more detailed, step-by-step guidance, you can find our other articles here:

• This AI Research Workflow Builds a 40-Page Report in 6 Minutes - For Under $1*

• How to Build an AI Assistant That Tracks Companies for You*

• Step-by-Step Debugging in Make.com: A Beginner’s Guide

• Master AI Automation: 10 Levels from Basics to Advanced

• You Don’t Need to Code to Automate High-Value Projects - Here’s How AI Automation Can Do It for You*

*indicates a premium content, if any