NLP & Generative Chatbot with RAG
Building an intelligent chatbot enhanced with Retrieval Augmented Generation
About This Project
This project focuses on building a generative model that can create text, images, or other content in response to prompts. The model is enhanced with Retrieval Augmented Generation (RAG) capabilities, allowing it to access and incorporate external information to improve response quality and factual accuracy. The system can dynamically retrieve relevant information from a knowledge base to augment its generated responses, providing more informed and contextually appropriate answers.
Core Concepts
- Generative AI models and their applications
- Retrieval Augmented Generation (RAG) architecture
- Vector databases for efficient information retrieval
- Prompt engineering techniques
- Context window optimization
- Hybrid retrieval-generation systems
Key Knowledge/Skills
- Generative modeling (GPUs, NVAs, diffusion models)
- Vector embedding techniques
- Content engineering, chunking, and retrieval design
- LLM prompt design and optimization
- Natural language processing
- Multi-modal content generation
Coursework Covered
Generative Models and LLMs (Advanced ML & AI Theory)