RAG: Enabling ChatGPT & LLM to Access Customized Knowledge

About RAG: Enabling ChatGPT & LLM to Access Customized Knowledge

This course is designed specifically for professionals who want to unlock the full potential of language models such as ChatGPT through Retrieval Augmented Generation Systems (RAGS). We will delve into how RAGS transform these language models into high-performance, expert tools across multiple disciplines by providing them with direct, real-time access to relevant, up-to-date information.

Importance of RAGS in Language Models

RAGS are fundamental to the evolution of large language models (LLMs), such as ChatGPT. Through the integration of external knowledge in real time, these systems enable LLMs to not only access a vast amount of up-to-date information but also learn and adapt to new information on a continuous basis. This retrieval and learning capability significantly improves text generation, allowing models to respond with unprecedented accuracy and relevance. This knowledge enrichment is crucial for applications that demand high accuracy and contextualization, opening up new possibilities in fields such as healthcare, financial analysis, and more.

Course Content

Generative AI and RAG Fundamentals

Introduction to assisted content generation and language models.

Classes on the fundamentals of generative AI, key terms, challenges and evolution of LLMs.

Impact of generative AI in various sectors.

In-depth study of Large Language Models

Introduction and development of LLMs, including base models and tuned models.

Exploration of the current landscape of LLMs, their limitations and how to mitigate common pitfalls such as hallucinations.

Access and Use of LLMs

Hands-on use of ChatGPT, including hands-on labs and access to the OpenAI API.

LLM Optimization

Advanced techniques for improving model performance, including RAG with Knowledge Graphs and custom model development.

Applications and Use Cases of RAGs

Discussion of the benefits and limitations of RAGs, with examples of real implementations and their impact in different industries.

RAG Development Tools

Instruction on the use of specific tools for RAG development, including No-Code platforms such as Flowise, LangChain and LlamaIndex.

Technical and Advanced RAG Components

Details on RAG architecture, indexing pipelines, document fragmentation and the use of embeddings and vector databases.

Hands-on Labs and Projects

Series of hands-on labs and projects that guide participants through the development of a RAG from start to finish, using tools such as Flowise and LangChain.

Methodology

The course alternates between theoretical sessions that provide an in-depth understanding of RAGS and hands-on sessions that allow participants to experiment with the technology in controlled, real-world scenarios.

This program is perfect for those who are ready to take the functionality of ChatGPT and other language models to never-before-seen levels of performance, making RAGS an indispensable tool in the field of artificial intelligence.

Requirements

No previous programming experience is required. The course will include the use of No-Code tools to facilitate the learning and implementation of RAGS.