Building Educational Content with Crew AI Framework

Crew AI empowers collaborative AI-driven content creation through specialized agents for research and planning.

### Building an Educational AI Content Framework with Crew AI
* 👋 In this second part of the series, the host welcomes Joe, the founder and CEO of Crew AI, to discuss the development of an **educational content portal** focused on **artificial intelligence**.
* 💻 The host shares their progress on the project, including the **successful use of Crew AI to generate articles and tutorials**, although they faced some challenges with **Python environments**.
* 🔍 They explain the structure of their project, which includes agents for **research and content creation**, and mention their desire to add a **reviewer agent** to enhance the process.
* 🤔 Joe provides insights on how to optimize the setup by **separating the research and writing phases**, suggesting the creation of a **planning stage** to improve collaboration between agents.
* 🛠 The duo discusses the use of Lang Trace AI for tracking progress and costs, and Joe emphasizes the **importance of observability** in AI projects.
* 💡 They delve into the specifics of creating a flow within Crew AI, demonstrating how to **set up different crews for research and content writing**, and how to **integrate existing code into the new framework**.
* 📊 As they work through the setup, they highlight the significance of **defining tasks and agents clearly**, ensuring that the output is **comprehensive and tailored to the intended audience level**.
* 🚀 Finally, they successfully execute the flow, generating research results and discussing the next steps, including the **integration of a PTIC object** to **enhance the functionality** of their AI agents.

### Streamlining Educational Content Creation with AI Agents
* 🤖 The discussion revolves around creating a **structured plan for generating educational content** using AI agents that can convert text input into specific **programming objects for further manipulation**.
* 📋 The content plan includes sections on **understanding large language models**, fine-tuning, techniques, and challenges, providing a **comprehensive outline for beginners**.
* 🛠 By utilizing function calling, the team can create objects that facilitate programming tasks, such as **loops**, making it **easier to manage the workflow** of different crews involved in content generation.
* 📈 The conversation highlights the importance of **refining the output from AI agents**, with specific attention to ensuring that only the **improved content is returned**, excluding any feedback or summaries.
* 💡 The team emphasizes the potential for future enhancements, such as **incorporating web scraping capabilities** for **better research and accuracy** in the generated content.
* 💬 "Sundar Pichai, the CEO of Google, said **25% of all of their code is now written by AI**," showcasing the **growing reliance on AI in programming**.
* 💻 The team discusses the efficiency of using smaller, cheaper models for most tasks, arguing that with **well-defined agent and task structures**, high-quality output can be achieved **without the need for more expensive models**.
* 📚 The final output is an educational content document that the team plans to **review and refine**, ensuring it meets the necessary standards for **clarity and depth**.

THIS is how you build AI Agents (part 2 with CrewAI Founder) 🤖

Artificial Intelligence (AI), Open Source, Generative Art, AI Art, Futurism, ChatGPT, Large Language Models (LLM), Machine Learning, Technology, Coding, Tutorials, AI News, and more

** Exclusive Pinecone Partner **

Matthew Berman

Building an Educational Content Portal with Crew AI

Joe, the founder of Crew AI, joins to help extend the educational content portal focused on artificial intelligence.

The portal's current setup includes a research agent and an educational content creator, with plans to add a reviewer agent.

Joe suggests separating the crew into a planning stage and a writing stage for more collaborative and effective content creation.

The speaker shares their positive experience with the Crew AI plugin, emphasizing its useful features like cost and time tracking.

Vulture, the video sponsor, offers GPU workload solutions with Nvidia GPUs across multiple locations, providing scalability and control.

The speaker and Joe discuss the process of creating a new flow in Crew AI, moving the existing crew files, and explaining the flow's function in facilitating data movement between crews.

The process involves two crews: one for research and planning, and the other for content creation.

The setup allows for advanced workflow management, with the possibility of using a single crew if desired.

The use of autocompletion and function renaming helps streamline the coding process, while a new environment is created for running the flow.

Creating a Research and Content Writing Crew

A more complex workflow can be established by adding a function that listens to generate educational content and making an API call to push it into a CMS.

A new crew for research and another for content writing are being created within the existing flow, resulting in a more compact setup.

The agents and tasks can be added to the crew files, with the goal of collecting necessary information and creating a plan for long-form content writing.

The process involves using two agents: one for thorough research and the other for creating a structured plan.

The research phase aims to gather a wide range of relevant information, while the planner will organize it into sections for the educational content.

The tasks include conducting research, providing leading-edge and beginner-friendly content, and delivering a comprehensive report with references for future content creation.

Planning Educational Content: A Step-by-Step Approach

The goal is to provide raw information for the next planning agent to create engaging educational content.

The planning task involves expanding each topic into a full section, tailored to different audience levels (beginner, intermediate, advanced).

The plan should be detailed and comprehensive, with each section having a title, subtitle, high-level goal, and referenced sources.

The crew's kick-off method needs to be called on an instance, requiring parentheses for proper instantiation.

The system offers features to update files with the right agents and tasks, streamlining the process.

A pantic object is suggested for better handling of specific keys and values, allowing for more efficient programming and looping through tasks.

Content Plan for Beginners: Understanding Large Language Models

The plan outlines the importance and real-world applications of large language models (LLMs).

It includes a detailed explanation of LLM architecture and the training process.

The content plan also covers fine-tuning, techniques, and challenges associated with LLMs, with a focus on creating a structured educational plan.

Building a Content Writing Crew from Research Agents

The process involves defining agents from the research crew and using their information to create a content writing team.

The new team includes roles like educational content writer, editor, and quality reviewer, with a focus on skill and audience level.

The workflow is streamlined by extracting input variables and utilizing the crew's functionality to replicate and exceed previous models' capabilities.

Optimizing Model Usage and Task Definitions

The approach involves starting with the best model and gradually transitioning to lower-cost models as efficiencies are found.

The framework allows for easy model changes by modifying a single line of code.

The discussion focuses on updating task definitions and agent names to streamline the workflow and improve content production quality.

The team is making adjustments to the AI content writer's tasks and agents for better efficiency.

They are focusing on passing the section variable to the writer crew and adjusting the task definitions to include the section.

The goal is to ensure the AI writes shorter sections that will eventually come together for a greater article.

Enhancing Agent Tools and Content Creation

The second crew could benefit from additional tools for expanding and fact-checking content.

A custom tool for searching previous documents and examples could also be helpful.

The idea of creating images, graphics, and diagrams from content is being explored, along with coding agents to automate data analysis and chart plotting.

The final output from the QA agent is being incorrectly considered as the actual final section, causing confusion.

The team is making adjustments to the expected outputs for the editor and quality review tasks to ensure they only provide the section content and incorporate feedback without including it.

The use of the Lang Trace module is being set up to track the workflow, and the team is discussing the model's effectiveness in achieving desired quality and control.

Smaller, more efficient AI models are capable of handling the majority of use cases, with some estimates suggesting they can address up to 98% of needs.

The use of agentic workflows and open-source models allows for cost-effective and in-house hosting, reducing data exposure concerns.

There are opportunities for improvement in the model's output, such as eliminating unnecessary bullet points and summaries, and tightening task definitions for greater efficiency.