Top 10 ChatGPT Use Cases In n8n You Didn't Know About

Key Concepts

• AI Image Generation & Manipulation
• Unstructured Data Extraction
• JSON Data Formatting
• AI-Powered Lead Scoring
• Retrieval Augmented Generation (RAG) Systems
• Text-to-Speech & Speech-to-Text Conversion
• Website Content Summarization
• Text Classification
• Custom GPT Integration

1. AI Image Generation and Manipulation

• Main Point: Using ChatGPT within Naden to generate and manipulate images for various purposes.
• Examples:
  • Generating lush product photos of soap and body wash.
  • Merging two AI-generated images into a new one.
• Details:
  • Images can be used for social media, blog posts, and designs.
  • AI can analyze images and generate relevant captions (e.g., for Instagram).
  • Example caption: "Self-care starts with little luxuries... Treat yourself to pure relaxation with this lush duo because you deserve it."
• Technical Terms: AI-generated images.

2. Unstructured Data Extraction

• Main Point: Extracting structured data from unstructured text using ChatGPT within Naden.
• Use Case: Extracting lead information from emails and entering it into a Google Spreadsheet.
• Process:
  1. Use the "Image Extractor" node (AI > Image Extractor).
  2. Define the data fields to extract (e.g., first name, last name, email, budget).
  3. The system automatically extracts the information and populates the spreadsheet.
• Limitations of Image Extractor: Limited to handling one data entry at a time.
• Solution: Using OpenAI's "Message a Model" for handling multiple data entries simultaneously.
• OpenAI Node Configuration:
  • System Message: Defines the parameters and instructions for ChatGPT (e.g., "Pull out first name, last name, email...").
  • User Message: Contains the unstructured data (e.g., the email content).
  • Assistant Message: Defines the structure of the output using JSON data.
• JSON Data: Used to standardize the output format for predictable data entry.
• Process for JSON Data:
  1. Ask ChatGPT to generate JSON data for the desired fields (e.g., "Please generate me JSON data to pull out first name, last name, email, and budget from multiple people at the same time.").
  2. Copy the generated JSON structure into the Assistant Message.
  3. Ensure the output content is formatted as JSON.

3. AI-Powered Lead Scoring

• Main Point: Using ChatGPT to score leads on a 1 to 5 scale based on predefined criteria.
• Use Case: Prioritizing leads based on their likelihood of conversion.
• Process:
  1. Define scoring criteria (e.g., revenue above $10,000, 5+ employees, budget).
  2. Assign points based on whether the lead meets the criteria.
  3. Use the system message to instruct ChatGPT on how to score the leads.
• System Message Example: "Hey, you need to rate this particular lead on a 1 to 5 point basis, and we're going to give you the criteria on how we want you to rate this person."

4. Retrieval Augmented Generation (RAG) Systems

• Main Point: Building a RAG system within Naden using ChatGPT.
• Difference between "Message a Model" and "Message an Assistant":
  • Message a Model: Generates one-time responses (like generating an image).
  • Message an Assistant: Creates a conversation thread with historical context and the ability to add tools (like RAG).
• Process:
  1. Create an assistant in OpenAI's platform (platform.openai.com > Playground > Assistants).
  2. Define the assistant's name, system instructions, and model.
  3. Upload files (e.g., FAQ documents) for the assistant to use as context.
  4. Configure memory settings (e.g., simple memory, MongoDB, Motorhead, Postgres).
  5. Use the thread ID to recall past messages.
• Tools: Code interpreter (enables the assistant to write and run code), functions.
• Example: Creating an assistant that answers customer questions based on an FAQ document.

5. Text-to-Speech & Speech-to-Text Conversion

• Main Point: Converting speech to text and text to speech using ChatGPT within Naden.
• Use Case: Creating an AI agent that can communicate via voice.
• Process:
  1. Download the audio file.
  2. Transcribe the audio file to text using a speech-to-text node.
  3. Generate a response using the AI agent's brain.
  4. Convert the text response to speech using a text-to-speech node.
  5. Send the audio file back to the user.

6. Website Content Summarization

• Main Point: Summarizing website content using ChatGPT within Naden.
• Limitation: ChatGPT API cannot directly search websites.
• Two-Part Process:
  1. Use an HTTP request to pull all the information from the website (HTML, CSS, text).
  2. Pass the website content to ChatGPT and ask it to summarize the content.
• Alternative: Using Perplexity (if available in Naden) to combine both steps into one.
• Perplexity Configuration: Choose a model in the sonar series.

7. Text Classification

• Main Point: Classifying text using ChatGPT within Naden.
• Use Case: Sorting incoming emails into different categories (e.g., sales, recruitment, accounting).
• Process:
  1. Receive an email.
  2. Use a text classifier node with ChatGPT to classify the email.
  3. Assign a label to the email based on the classification.

8. Custom GPT Integration

• Main Point: Connecting custom GPTs built in the ChatGPT interface to Naden.
• Requirement: Requires a ChatGPT Plus subscription.
• Process:
  1. Build a custom GPT in the ChatGPT interface.
  2. Connect the custom GPT to an external AI agent inside Naden.
  3. Give the AI agent access to various tools within Naden.
• Example: Scheduling a coffee event on a calendar using a custom GPT connected to Naden.

9. Conclusion/Synthesis

The video demonstrates ten practical use cases of integrating ChatGPT with Naden for business automation. These use cases range from AI-powered image generation and manipulation to advanced data extraction, lead scoring, RAG systems, and custom GPT integrations. The key takeaway is that by leveraging ChatGPT's capabilities within Naden, businesses can automate various tasks, improve efficiency, and focus on higher-value activities. The video emphasizes the importance of understanding the different nodes and configurations within Naden, as well as the proper formatting of data (e.g., JSON) for optimal results. The presenter also promotes his school community for those seeking to further their knowledge in AI automation.