RAG: Enabling ChatGPT & LLM to Access Customized Knowledge

About this course

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

Why RAG mattersRAG is fundamental to the evolution of large language models like ChatGPT. By integrating external knowledge in real time, these systems allow LLMs to access vast, up-to-date information and adapt continuously to new data, without retraining. This significantly improves accuracy and relevance — critical for applications in healthcare, financial analysis, legal, and any field where hallucinations are unacceptable.

What you'll learn

The fundamentals of Generative AI, LLMs, and how RAG solves their core limitations, including hallucinations

Hands-on use of ChatGPT and the OpenAI API

How to build Custom GPTs enriched with your own knowledge baseRAG architecture end-to-end: indexing pipelines, document fragmentation, embeddings, and retrieval

How to work with Vector Databases — Pinecone, Vectara, and how to choose the right one for your use case

How to build RAG systems both with code (LangChain, LlamaIndex) and no-code (Flowise)How to deploy RAG on-premise for maximum data privacy, using Docker, LocalAI, GPT4All, and local Llama2 models

How to evaluate RAG system performance using RAGAS and the TrueLens RAG TriadHands-on labs and projects

This is not a theory-only course. You'll complete a full personalized chatbot assistant project from start to finish — combining web scraping, Pinecone, and Conversational Retrieval QA Chains — plus dozens of hands-on labs building i

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