Update/News: Check the Live RAG Webinar details here.
RAG (Retrieval-Augmented Generation) is gaining traction as a method to enhance large language models by integrating external data retrieval, leading to more accurate and contextually relevant outputs.
Are you interested in becoming an expert in RAG?
I have decided to do live coaching of RAG. I am considering both Group/Batch coaching and One-on-One coaching. Contact me (Rajamanickam.a@gmail.com) for the details.
Initially, I will start with one-on-one coaching, then will start Group coaching based on the demand.
If you are unfamiliar with AI, you can get my recorded AI course before joining this RAG Live coaching.
Find below the Outline of the coaching. I will update it based on students' feedback.
Introduction to RAG
- What is Retrieval-Augmented Generation?
- Why RAG is essential for modern AI systems
- Overview of RAG architecture and components
- Setting up the development environment
Retrieval Mechanisms
- Understanding vector stores and embeddings
- Integrating retrieval models (e.g., using Pinecone or ChromaDB)
- Hands-on: Implementing a basic retrieval pipeline
Augmenting Generation Models
- How to combine retrieval with generative models
- Fine-tuning language models for better retrieval integration
- Hands-on: Building a RAG-powered chatbot
Advanced Techniques and Applications
- Contextual memory and long-form content generation
- Multimodal RAG: Integrating text, images, and more
- Hands-on: Building a knowledge-based assistant
Evaluation and Optimization
- Measuring the effectiveness of RAG systems
- Fine-tuning for accuracy and relevance
- Hands-on: A/B testing and performance tuning
Capstone Project and Deployment
- Building a fully functional RAG application
- Deploying on cloud platforms (e.g., AWS, GCP)
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