Project Title

Build AI Automation and AI Agents

Project Overview

In this class, you will complete two hands-on projects. The first project focuses on building AI automation and AI agents using no-code tools. The second project focuses on building AI automation and AI agents using Python and large language models (LLMs). You can follow both projects or focus on one, depending on your experience and learning goals. By the end of these projects, you will have working AI systems that solve real-world problems and can be reused or expanded.

Project 1: Build an AI Automation or AI Agent Using No-Code Tools Overview

In this project, you will build an AI automation or AI agent without writing code. This project is ideal if you want fast results and practical workflows using popular automation platforms.

Steps to Complete Project 1

1. Choose What You Want to Build
Decide on one AI automation or AI agent, such as AI appointment scheduling automation, AI data entry automation, AI customer support agent, AI content writer agent, AI market research agent, or any idea of your choice.

2. Select Your No-Code Tools
Choose one or more tools to build your automation or agent. You can use Zapier, Make, n8n, Relay App, or Gumloop.

3. Design the Workflow
Define the trigger, input data, AI processing step, and final action. Decide how the AI should respond and what output it should generate.

4. Build and Test Your Automation
Create the workflow inside your chosen tool. Test it with real examples to make sure the automation runs correctly and produces useful results.

5. Finalize and Share
Prepare screenshots or short explanations of your workflow and upload them to the Skillshare Project Gallery. Explain what you built, which tools you used, and how the automation helps solve a real problem.

Project 2: Build an AI Automation or AI Agent Using Python and LLMs Overview

In this project, you will build an AI automation or AI agent using Python and large language models. This project is suitable if you want more control, customization, and deeper technical understanding.

Steps to Complete Project 2

1. Choose Your Use Case
Decide what AI automation or AI agent you want to build, such as AI data analysis agent, AI research agent, AI content generation agent, AI planning or project management agent, or any other idea you prefer.

2. Select Your LLM
Choose a large language model to power your agent, such as Mistral, Llama, Gemini, or another LLM.

3. Decide on Context Handling
If your AI needs detailed context, long-term memory, or domain knowledge to perform well, you can integrate a vector database. Tools like Pinecone or Chroma allow the AI to store information, retrieve relevant data using similarity search, and generate more accurate, context-aware responses.

4. Build the Automation or Agent in Python
Implement the logic using Python. Define inputs, prompts, tools, memory handling, and outputs. If using a vector database, connect the LLM to Pinecone or Chroma to enable knowledge retrieval.

5. Test and Improve
Test your agent with different scenarios to ensure it responds correctly, handles context properly, and performs the intended task reliably.

6. Finalize and Share
Upload code snippets, explanations, or screenshots to the Skillshare Project Gallery. Describe the problem you solved, the LLM you used, whether you integrated a vector database, and what you learned from building the project.

M HASSAN ZUBAIR

1

3