Automate ANY task using ChatGPT! (with GPT actions feature)

How to Automate Tasks with ChatGPT

Introduction to Automation with ChatGPT

• ChatGPT is a powerful tool for automating tasks, but effective usage is crucial to avoid time wastage from unnecessary copying and pasting.

• Users can retrieve relevant information from various sources like WordPress, Google Docs, and social media without leaving the ChatGPT interface.

• The video promises a step-by-step guide along with resources and checklists for successful automation in ChatGPT.

Importance of AI Foundations Community

• The AI Foundations Community aims to enhance efficiency and productivity in both personal and work-related tasks through proper AI utilization.

• Viewers are encouraged to join the community via links provided in the description for further learning on leveraging AI effectively.

Understanding Requests: GET vs. POST

• A brief explanation of how requests work within ChatGPT: GET requests fetch information while POST requests send data to other applications.

• Example of a GET request: Asking how many new emails are present today, which triggers an automated process in Gmail to retrieve email counts.

• Example of a POST request: Writing a story about a dog that gets sent directly to Google Docs upon completion.

Setting Up Automation

• A checklist is provided for viewers to follow along during the video, including creating custom GPT setups tailored for specific tasks.

• Identifying what needs automation is essential; an example given involves aggregating news articles into ChatGPT for automatic LinkedIn posts.

Creating Your Custom GPT

• Step one involves naming your GPT, providing descriptions, instructions, conversation starters, and selecting profile pictures as part of setup.

• Detailed guidance on creating an effective GPT includes understanding prompt engineering basics before diving into complex automations.

How to Automate Article Fetching with GPT and Make.com

Setting Up the Automation Process

• The speaker introduces a command to fetch recent articles on AI, emphasizing the importance of this function in streamlining tasks.

• A process is described where the system reads content from a URL and formats it for LinkedIn posts, showcasing a custom output format for each post.

• Completion of initial setup steps is noted, including naming, description, profile picture, instructions, and conversation starters as part of the automation checklist.

Creating Webhooks in Make.com

• The speaker discusses creating a webhook in Make.com to facilitate automation. This step may take 10-20 minutes but saves significant time later.

• Emphasis is placed on setting up a GET request to retrieve recent articles based on user commands like "fetch articles."

• Instructions are provided for configuring settings within Make.com to establish the GET request properly.

Configuring Webhook Settings

• The speaker explains using a free account on Make.com while utilizing GPT Plus for enhanced capabilities.

• Steps include creating a new scenario and selecting custom webhooks; advanced settings are adjusted accordingly.

• Naming conventions for webhooks are discussed, with an example name "posting for LinkedIn" provided.

Integrating Various Applications

• After establishing the webhook URL, various applications can be integrated (e.g., Facebook, Instagram), allowing media downloads and database item creation in Notion or Google Sheets.

• The tool RSS doapp is introduced as an aggregator for news articles from multiple sources like MIT News and TechCrunch.

Finalizing Automation Setup

• The speaker details how to set up feeds that pull information from specified URLs while customizing parameters such as date ranges and maximum returned items.

• Data formatting is crucial; only one article can be pulled at once unless aggregated into one text strand.

• A reminder that many users may not need complex setups; simpler configurations can suffice depending on individual needs.

Completing Webhook Response Configuration

• Once automation is established through three main steps, attention turns to creating a webhook response that maps data correctly.

Creating Dynamic Automations with Webhooks

Setting Up the Automation

• The response from the application will be received in GPT, allowing for specific data mapping to your webhook response, enhancing automation capabilities.

• The next step involves creating a schema for the GET request to pull information into GPT, specifically targeting LinkedIn content.

Connecting to GPT Action

• To connect the automation, you need to create an OpenAI schema for the GET request as instructed by Schema Ninja.

• A screenshot of the desired information (URL, title, author) must be uploaded or specified directly to inform what data should be pulled.

Creating and Uploading Schema

• You can provide detailed instructions on what information is needed for schema creation; this includes article URL, author, and content.

• After providing necessary details and your Make webhook URL from earlier steps, you can proceed to create your schema in JSON format.

Testing Your Automation

• Once the schema is created successfully without errors, it can be pasted into the action section of your GPT setup.

• Testing involves triggering the webhook which executes the automation process and returns results based on input parameters.

Finalizing Setup and Adjustments

• After testing successfully with a status code of 200 indicating proper function, adjustments can be made if data does not appear as expected.

• Instructions may need refinement; for instance, asking for article details before generating LinkedIn posts enhances control over output.

Fetching Articles

• Initiating a test fetch will allow verification that all requested data (author and title hyperlinks included) is being retrieved correctly.

Creating LinkedIn Posts with Automation

Designing Protein-Related LinkedIn Posts

• The discussion begins with the potential to create a LinkedIn post centered around designing proteins that could revolutionize medicine. The initial focus is on generating content based on an article.

Setting Up Actions for Posting

• A question arises about sending the generated post as a LinkedIn update, highlighting that the current setup lacks this action. The checklist indicates progress in creating and testing GPT actions.

Revising Schema for HTTP Requests

• To enable posting, there’s a need to revise the existing schema, which currently only supports GET requests. This limitation necessitates adding another method for HTTP requests.

Dynamic Mapping of Webhook Data

• The speaker emphasizes the importance of pulling dynamic LinkedIn posts into the webhook by revising automation and ensuring test data is available for mapping fields effectively.

Adding POST Method to Existing Schema

• Instructions are provided on how to add a POST request method to the existing schema, allowing users to send content (LinkedIn posts) through their webhook.

Structuring JSON Format for Posts

• It’s crucial to format information in JSON when creating LinkedIn posts. Required fields include "post title" and "post content," which must be structured correctly for successful data transmission.

Testing Post Request Functionality

• After setting up the schema and instructions, testing of the POST request is conducted. An example post is sent successfully, confirming that the system can now handle posting actions effectively.

Creating Automation from Received Data

How to Set Up Automated LinkedIn Posting

Setting Up Routes for Requests

• The speaker discusses adding a LinkedIn integration, creating a user text post, and establishing two routes: one for POST requests and another for GET requests.

• A filter is set up on the POST request to ensure it processes correctly. The condition is labeled as "condition setting," with the method specified as POST in a case-insensitive manner.

Testing Data Flow

• To verify data transmission, the speaker emphasizes sending data again to ensure fields are populated correctly in the test feed.

• If dynamic content isn't pulling in, saving the automation and resending data from GPT is recommended to refresh connections.

Automating Post Creation

• The process of dynamically pulling in post content from the webhook is demonstrated. The speaker saves this automation before testing it further.

• After completing tests, access settings can be adjusted (e.g., making it invite-only), followed by fetching recent articles for posting.

Generating Content Automatically

• The system fetches recent articles from an RSS feed, allowing users to create posts based on these articles without leaving the GPT interface.

• Users can revise generated posts using ChatGPT's assistance, including attributing content properly and providing links instead of generic attributions.

Finalizing Posts and Community Engagement

• Upon confirming details, users can send their posts directly to LinkedIn through the webhook. This automated process showcases efficiency in AI-driven tasks.