Gen AI Course | Gen AI Tutorial For Beginners

Introduction

This tutorial provides a comprehensive overview of Generative AI (Gen AI) for beginners. It covers fundamental concepts, the evolution of Gen AI, and practical applications through two end-to-end projects. By the end of this tutorial, you will have a solid understanding of Gen AI and the skills to implement basic projects.

Step 1: Understand Generative AI

• Definition: Generative AI refers to algorithms that can generate new content, such as text, images, or music, by learning from existing data.
• Relevance: It is widely used in various applications, including content creation, data augmentation, and interactive systems.

Step 2: Explore the Evolution of Gen AI

• Historical Context: Learn how Gen AI has advanced from simple rule-based systems to complex models capable of understanding and generating human-like content.
• Key Milestones: Familiarize yourself with significant developments in AI technology that have shaped the current landscape.

Step 3: Learn About Large Language Models (LLM)

• Definition: LLMs are a type of AI model trained on vast amounts of text data to understand and generate language.
• Functionality: They can perform tasks like translation, summarization, and even creative writing.

Step 4: Understand Embeddings and Vector Databases

• Embeddings: These are numerical representations of data (like words or phrases) that capture semantic meaning.
• Vector Database: This technology allows for efficient storage and retrieval of embeddings, enabling faster search and matching of similar items.

Step 5: Grasp Retrieval Augmented Generation

• Concept: This technique combines the strengths of retrieval systems and generative models, allowing AI to generate more accurate outputs by fetching relevant context or data.
• Application: It is particularly useful in chatbots and virtual assistants where context is crucial.

Step 6: Familiarize Yourself with Tooling for Gen AI

• Tools Overview: Explore various tools and frameworks that facilitate the development of Gen AI applications.
• Considerations: Choose tools based on the specific requirements of your projects, such as scalability, ease of use, and community support.

Step 7: Dive into LangChain Fundamentals

• What is LangChain?: LangChain is a framework designed to simplify the development of applications using LLMs and vector databases.
• Key Features:
  • Modular architecture for easy integration of components.
  • Built-in tools for managing data flow and context handling.

Step 8: Build Project 1: Equity Research Tool

• Access the Code: Find the project code here.
• Implementation Steps:
  1. Set up your development environment.
  2. Clone the repository from GitHub.
  3. Follow the instructions in the README file to run the project.

Step 9: Build Project 2: Retail Q&A Tool

• Access the Code: Find the project code here.
• Implementation Steps:
  1. Set up your development environment similarly to Project 1.
  2. Clone the repository from GitHub.
  3. Use the provided instructions to run the project.

Conclusion

In this tutorial, you learned the fundamentals of Generative AI, including key concepts like LLMs, embeddings, and retrieval-augmented generation. You also gained hands-on experience through two projects using the LangChain framework. To further enhance your skills, consider exploring more advanced topics or projects in the field of AI. Happy learning!