RAG: Enabling ChatGPT & LLM to Access Customized Knowledge

Learn about Retrieval-Augmented Generation to enrich the knowledge of ChatGPT and LLMs, increasing their effectiveness

Created by: Data Bootcamp
data scientist

What Will I Learn?

• Introduction to Generative AI and Large Language Models
• Techniques for Improving LLMs
• Fundamentals of Retrieval Augmented Generation (RAG)
• Applications of RAGs
• Tools for the development of a RAG
• Custom GPTs
• Langchain
• Components of the RAG
• Flowise the perfect framework for the development of RAGs
• Indexing Pipeline and RAG Pipeline
• Document Fragmentation
• Embeddings and Vector Databases
• Information search and retrieval
• Open-source LLMs for RAGS: the best ally for data protection and privacy
• RAG performance evaluation

Requirements

• not needed

Target audience

• Technology and Artificial Intelligence Professionals: Ideal for those working in the fields of AI, machine learning, software development and information technology who are looking to integrate and optimize advanced capabilities in their systems.
• Software Developers: Especially those interested in improving the functionality and accuracy of applications based on natural language processing (NLP) and language models.
• AI Enthusiasts and Autodidacts: People with a general interest in artificial intelligence and emerging technologies who want to learn about the latest innovations and their practical application.