รู้จัก "RAG" AI ที่จะพลิกโฉมการ Search ข้อมูล !!

Introduction

This tutorial will guide you through understanding RAG AI and how to enhance your AI's knowledge without the need for expensive fine-tuning. We will explore the differences between various approaches such as prompt engineering, fine-tuning, and RAG (Retrieval-Augmented Generation). By the end of this tutorial, you should have a clear understanding of how to leverage these techniques effectively.

Step 1: Understanding RAG AI

• RAG combines the strengths of retrieval and generation to improve the performance of AI models in searching and processing information.
• It utilizes a vector database to retrieve relevant information, allowing the model to generate more accurate and contextually relevant responses.

Step 2: Exploring Prompt Engineering

• Prompt engineering involves crafting specific inputs (prompts) to guide the AI in generating desired outputs.
• Key aspects to consider:
  • Clarity: Make prompts clear and specific.
  • Context: Provide sufficient context to help the model understand the task.

Step 3: Differentiating Fine-Tuning from RAG

• Fine-tuning:
  • Involves adjusting the AI model's parameters using a specific dataset.
  • Often requires significant resources and expertise.
• RAG:
  • Does not require modifying the model directly.
  • Leverages external data and retrieval methods to enhance responses.

Step 4: Creating a Vector Database

• A vector database stores data in a way that allows for efficient searching and retrieval.
• Steps to create a vector database:
  1. Choose a suitable database platform (e.g., Pinecone, Weaviate).
  2. Prepare your data by converting it into vectors using an embedding model.
  3. Index the vectors in your database for quick retrieval.

Step 5: Implementing Retrieval and Embedded Data

• Retrieval involves fetching relevant data based on a query.
• Embedded data refers to the representation of information in a numerical format (vectors).
• Steps to implement:
  1. Use a pre-trained model to convert text data into embeddings.
  2. Store these embeddings in your vector database.
  3. When making a query, retrieve the closest embeddings and use them to inform the AI's response.

Step 6: Joining Live Workshops for Further Learning

• Engage in live workshops for hands-on experience and deeper understanding.
• Share your questions and learn from the community.
• Follow the video channel for updates on future workshops.

Conclusion

RAG AI presents a powerful method for enhancing AI capabilities while minimizing costs associated with fine-tuning. By mastering prompt engineering and understanding the differences between various AI training methods, you can significantly improve your AI’s performance. Start experimenting with vector databases and retrieval methods to see tangible benefits in your applications. As you continue learning, consider joining community workshops for shared insights and support.