Crie um Agente de IA no ChatGPT e Melhore Suas Pesquisas Avançadas

Creating a Super Agent GPT for Research Automation

Introduction to the Concept

• The video introduces a method to automate research by creating a "Super Agent GPT" that generates Google Forms directly from chat interactions.

• Presenter Sandeco, an educator and researcher, aims to assist viewers in utilizing AI in their daily tasks, specifically focusing on research automation.

Workflow Overview

• The workflow involves two main entities: the researcher (user) and the Super Agent. The researcher can conduct various types of research including market, scientific, and social studies.

• The Super Agent has two profiles: one as a researcher that helps create surveys and another as an analyzer that processes collected data.

Functionality of the Super Agent

• As a researcher, the agent assists in formulating questions for surveys—covering open-ended, closed-ended, quantitative, and qualitative formats.

• As an analyzer, it receives survey data from users and utilizes machine learning within ChatGPT to analyze this data effectively.

Machine Learning Integration

• The unique aspect is that the Super Agent trains its own machine learning model based on user-provided data while operating within ChatGPT.

• This process allows users to download trained models for use in other programming environments like Python.

Practical Implementation Steps

• Initially using ChatGPT 4 but also compatible with version 3.5; viewers are encouraged to stay tuned for instructions on both versions.

• A prompt example is provided for creating a professional profile focused on marketing research; users can adapt this template for different fields of study.

Community Support

Introduction to Data Research and AI Tools

Overview of the AI Agent

• The speaker introduces an AI agent created from a competition within a group focused on images generated by Artificial Intelligence.

• Acknowledgment of Marcelo, the winner of the competition, who contributed to creating a significant image for the project.

Prompt Structure

• The prompt begins with a structured interaction format, emphasizing user assistance in maximizing data research potential.

• Users are prompted to choose from various types of market research including segmentation, product pricing, promotion, and distribution.

Research Types and User Engagement

• Each research type is assigned a unique value; users can select based on their specific needs.

• Upon starting the interaction, users receive guidance on initiating new research projects tailored to their interests.

Conducting Targeted Research

Focus on Campus Par Event

• The speaker discusses conducting segmentation research related to the Campus Par event across multiple cities in Brazil.

• Identifies target segments such as gamers, developers, entrepreneurs, content creators, students, and tech enthusiasts.

Generating Research Briefings

• The AI agent is tasked with generating a briefing for the segmentation research focusing on objectives and key questions.

• Emphasizes collecting data through structured methods while outlining success criteria and necessary resources.

Creating Google Forms for Data Collection

Form Design Specifications

• Instructions are given to create a Google Form with ten objective questions; only one question will ask for numerical age data.

Script Generation Process

• The process involves generating scripts that automate form creation without manual coding efforts from users.

Execution of Scripts

• Users are guided through executing scripts in Google Apps Script to streamline form generation efficiently.

• Highlights ease of use where users simply need to run scripts rather than manually inputting each question into forms.

Final Steps in Form Creation

Completing the Setup

• Final instructions include logging into Google Apps Script and creating new projects seamlessly using provided scripts.

Analyzing Survey Data with AI

Introduction to the Analysis Process

• The speaker introduces a link to access a form created by their "super agent" for analyzing survey data.

• Emphasizes the importance of subscribing to their channel and engaging with the content, hinting at valuable insights in the upcoming sections.

Importing and Preparing Data

• The process begins by importing a CSV file generated from Google Forms, which contains survey results.

• Highlights that having detailed data allows for advanced techniques like preprocessing, statistical analysis, and machine learning.

Data Consistency Checks

• The speaker initiates reading the data and checks for inconsistencies, noting past experiences with erroneous entries (e.g., grades outside expected ranges).

• Discusses how the AI will identify any inconsistencies in user responses, ensuring data integrity before proceeding.

Preprocessing Steps

• The AI confirms no inconsistencies were found; all fields are appropriately formatted except for age, which is an integer.

• The speaker requests preprocessing of categorical data to prepare it for machine learning algorithms.

Machine Learning Implementation

• After preprocessing, the next step involves creating a machine learning model to cluster users based on their survey responses.

• A simple command triggers the generation of a machine learning model using an algorithm known as K-means clustering.

Insights from Clustering Results

• The AI successfully trains on provided data and identifies two distinct groups among respondents.

• Offers to delve deeper into these clusters' characteristics and insights derived from them.

Cluster Analysis Overview

• Describes Cluster 0: consists of 47 participants with diverse interests leaning towards entrepreneurship and research.

• Notably includes preferences for in-person events and significant interest in blockchain technology.

• Describes Cluster 1: comprises 56 participants primarily from IT backgrounds with an average age of 24.

Analysis of Insights from Data Clusters

Overview of the Analysis Process

• The speaker instructs to analyze information and clusters to extract insights based on original data, emphasizing a general search for insights beyond current observations.

Personalization in Event Experience

• Discussion highlights the diversity of interests and the need for personalization, suggesting that Campus in Brazil could enhance event experiences by tailoring them to specific interests such as entrepreneurship, information technology, and robotics.

Preferences for Event Formats

• There is a notable division among participants regarding their preferred event format, with emphasis on promising future areas like blockchain and artificial intelligence. The importance of hybrid experiences is also mentioned.

Cluster Analysis

• The first cluster identified is labeled as "entrepreneurs," which is smaller than the "developers" cluster. This indicates varying levels of engagement or representation within these groups.

Visual Representation of Data