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Automate AI Research Agents (Full Guide)
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Automate AI Research Agents (Full Guide)

New Section

In this section, the speaker introduces the topic of creating an AI research agent using Perplexity AI and Make.com to automate content creation processes.

Introduction to AI Research Agent

  • The tutorial focuses on creating an AI research agent with numerous use cases using Perplexity AI and Make.com.
  • The goal is to feed Perplexity AI a prompt and URL, such as a YouTube video or Reddit post, to generate diverse content outputs.
  • After inputting the URL, the process involves transposing the content into a Google doc script and sending it to different GPT specialist agents for varied content creation like LinkedIn posts, tweets, and Facebook content.

Setting Up Automation Tools

This part delves into setting up the no-code automation tool Make.com for integrating Perplexity AI seamlessly.

Setting Up Make.com

  • Make.com is introduced as a no-code automation tool similar to Zapier, allowing drag-and-drop functionality for connecting various apps.
  • Users can access features like connecting Gmail, Google Docs, and other apps through Make.com without any coding requirements.
  • The process involves visiting the Make.com homepage, creating a new scenario, and adding Perplexity AI by searching for it within the platform.

Integrating Perplexity AI

This section covers integrating Perplexity AI with Make.com for enhanced automation capabilities.

Integrating Perplexity AI

  • Integration of Perplexity AI requires accessing its Community tab via a third-party website for €37 to enable usage across Chrome integrations.
  • Upon payment confirmation, users receive step-by-step instructions along with a unique access code for integration.
  • Additional steps involve adding API credits in Perplexity account settings before returning to Make.com where the Perplexity icon appears for activation.

Utilizing Chat Completion Feature

Exploring how to leverage chat completion feature within Perplexity AI for generating content on personal finance topics from Reddit forums.

Leveraging Chat Completion

  • Demonstrates utilizing chat completion feature by selecting relevant prompts like personal finance topics from Reddit forums.
  • Users can extract URLs from chosen topics and assign roles like summarization expert within the chat completion interface.

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In this section, the speaker discusses running a module using no-code automation tools and the flexibility of scheduling its execution at desired intervals.

Running Modules with No-Code Automation Tools

  • Click "okay" to run the module.
  • Emphasizes the flexibility of running modules as frequently as desired (e.g., every 50 minutes, hour, or week).
  • Demonstrates checking the output in the output section after running the module.

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The speaker explores customizing prompts and generating content summaries based on user input.

Customizing Prompts and Content Summaries

  • Discusses editing prompts for customization.
  • Shares an example post summary about enrolling in a 401K plan.
  • Mentions the ability to modify prompts for desired outputs.

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This part delves into insights gained from others' experiences regarding financial advice and retirement planning.

Insights on Financial Advice and Retirement Planning

  • Highlights experiences shared by others.
  • Emphasizes starting early and maximizing contributions for retirement planning.
  • Discusses tax benefits associated with 401K contributions made with pre-tax dollars.

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The speaker elaborates on utilizing data extracted from URLs for various purposes like creating content or conducting research.

Utilizing Data from URLs

  • Summarizes posts, comments, and data extracted from URLs.
  • Mentions potential uses such as creating videos or adding content to Google Docs.

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Exploring integrating automation tools to streamline content creation processes efficiently.

Integrating Automation Tools for Content Creation

  • Demonstrates adding content to Google Docs through automation.
  • Shows appending text from previous modules into documents seamlessly.

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Automating document updates through continuous integration of new content generated by automation tools.

Automating Document Updates

  • Executes automation process to update Google Docs with new content regularly.

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Discussing continuous updating of documents with fresh content generated through automation processes.

Continuous Document Updating Through Automation

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In this section, the speaker discusses the process of utilizing a prompt to feed into a chat GPT agent for detailed outputs and emphasizes the importance of providing specific instructions to the bot.

Claude's Prompt Instructions

  • The speaker introduces Claude, who will provide a prompt for feeding into the chat GPT agent.
  • Emphasizes the significance of receiving detailed outputs by experimenting with and potentially modifying the prompt.
  • Instructions are given to only respond with content necessary for the bot, highlighting the importance of clear and concise instructions.

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This segment focuses on Cloe presenting a problem statement and outlining desired output characteristics such as optimal length, clarity, logical flow, and maximum impact.

Cloe's Problem Statement and Output Criteria

  • Cloe presents a problem statement aimed at generating required outputs efficiently.
  • Describes desired output characteristics including an optimal length of 1300 characters or less, clarity, logical flow, and maximum impact.

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Here, there is an emphasis on completing tasks once to enable automation in perpetuity without constant revisiting.

Task Completion for Automation

  • Completing tasks ensures minimal need for subsequent adjustments as automation runs continuously in the background.
  • Highlights that after task completion, automation operates seamlessly without requiring frequent manual interventions.

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This part delves into replicating processes across different platforms like Facebook and X while emphasizing structured content creation techniques.

Replicating Processes Across Platforms

  • Discusses replicating processes on various platforms like Facebook and X to ensure consistency in approach.
  • Emphasizes structured content creation techniques including post structure editing for clarity and logical flow optimization.

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The focus here is on selecting appropriate models like GPT4 Turbo Preview for enhanced outcomes despite potential additional costs.

Model Selection for Enhanced Outcomes

  • Introduces selecting models like GPT4 Turbo Preview despite potential higher costs due to improved performance benefits.
  • Mentions negligible cost implications despite opting for advanced models like GPT4 Turbo Preview.
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Video description

This is a step-by-step tutorial on how to build an AI research agent using Perplexity AI and Make.com. The automation works by feeding Perplexity AI a prompt and URL, which it analyzes to provide a summary, comment insights, and additional research data. This output is then sent to a Google Doc and passed through a router to three different GPT specialist agents that transform the content into posts for LinkedIn, Twitter, and Facebook. To set this up, I used Make.com, a no-code automation tool, and integrated Perplexity AI (which required a one-time fee). I also connected an OpenAI API key to utilize GPT-4 for the specialist agents. For the LinkedIn bot, I used Claude AI to generate custom instructions on how to structure the post, considering factors like optimal length, clarity, and engagement. The process is highly customizable and scalable, with countless potential applications. While there are some costs involved, such as Make.com's monthly fees, ChatGPT Plus, and Perplexity AI credits, I found them to be reasonable for the level of automation achieved. I believe this AI research agent has significant potential and I'm excited to see how others might adapt it for their own purposes.