RAG: Raising the Potential of ChatGPT LLMs to the next level

Learn how to implement RAGs to enrich the knowledge of ChatGPT and LLMs, increasing their effectiveness and capabilities


What you'll 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

Description
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.
Who this course is for:
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.

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