I Built an AI Assistant That Knows Everything I’ve Taught

I built my own AI assistant, Alex. She can talk to you naturally and has access to the knowledge I've shared through my lectures on System Design, Java, Spring Boot, GenAI, FDE and more. But the interesting part isn't just the avatar. The real question is: how do you actually build a system like this? In this video, I break down the complete architecture behind Alex — from turning my existing content into a searchable knowledge base to retrieving the right information, generating an answer, understanding human speech and finally making an AI avatar respond naturally. We’ll understand how the complete system comes together: → My lectures and existing content as the knowledge source → Embeddings → Vector Database → RAG (Retrieval-Augmented Generation) → LLM for understanding and response generation → Speech-to-Text (STT) → Text-to-Speech (TTS) → Interactive AI Avatar → How all these components communicate to create one complete experience The first few minutes of the video are a demo of Alex in action, followed by a complete breakdown of the architecture behind the project. This is not a code-along tutorial. The objective is to understand how different GenAI components can be connected together to build a real AI product. Source Code: https://github.com/adityatandon15/ai-assitant Create your own Interactive Avatar using Synthesia : https://www.synthesia.io/features/avatars/interactive-avatars?utm_source=youtube&utm_medium=creator&utm_campaign=interactive_avatar_api&utm_content=coder_army If you've been learning concepts like RAG, embeddings, vector databases, LLMs, STT and TTS separately, this project shows how they can all come together inside one application. #AI #GenerativeAI #RAG #LLM #ArtificialIntelligence