AI Engineering Fundamentals to Production

• AI Course Roadmap
  
• Module 1.1 — AI vs ML vs Deep Learning vs LLM
  
• Module 1.2 — What is Generative AI & How ChatGPT Actually Works
  
• Module 1.3 — Tokens, Context Window & Temperature
  
• Module 1.4 — Prompts: System Prompt vs User Prompt vs Completion
  
• Module 2.1 — What is NLP, Words vs Tokens & Tokenization
  
• Module 2.2 — Vocabulary, Embeddings, Parameters & Model Weights
  
• Module 2.3 — The Transformer Architecture
  
• Module 2.4 — The Attention Mechanism
  
• Module 3.1 — What is an Embedding & Why Does it Exist
  
• Module 3.2 — Vectors, Vector Space & Dimensions
  
• Module 3.3 — Similarity Search: Cosine, Euclidean & Dot Product
  
• Module 3.4 — Practical: Convert Words to Vectors & Compare Them
  
• Module 4.1 — Why SQL is Not Enough
  
• Module 4.2 — What is a Vector Database & How it Works
  
• Module 4.3 — ANN Search, Top-K & Metadata Filtering
  
• Module 4.4 — Hands-on: Chroma Local Vector Database
  
• Module 5.1 — What is RAG & Why it Exists
  
• Module 5.2 — Chunking
  
• Module 5.3 — Retrieval & Context Injection
  
• Module 5.4 — Hallucination & Grounding
  
• Module 5.5 — Project: Full PDF Chatbot
  
• Module 6.1 — What is LangChain & What Problem it Solves
  
• Module 6.2 — Models, Prompts & Chains
  
• Module 6.3 — Output Parsers, Memory & Tools
  
• Module 6.4 — Document Loaders, Text Splitters & Vector Stores
  
• Module 6.5 — Retrievers & Agents in LangChain
  
• Module 7.1 — Production RAG System with LangChain
  
• Module 7.2 — Streaming, Error Handling & Express API
  
• Module 7.3 — Next.js Frontend for the RAG Chatbot
  
• Module 7.4 — Pinecone Cloud Vector Database
  
• Module 8.1 — What is an AI Agent & The ReAct Pattern
  
• Module 8.2 — Tool Calling & Function Calling Deep Dive
  
• Module 8.3 — Agent Memory & Planning
  
• Module 8.4 — Multi-Agent Systems with LangGraph