Mastering Generative AI & Large Language Models

Direct Course Definition: Advanced engineering masterclass covering modern transformer architectures, large language models (GPT, Llama, Qwen), LoRA and QLoRA fine-tuning, prompt engineering, embeddings and vector databases (pgvector/Pinecone), RAG architectures, autonomous AI agents, and production AI serving.

Generative AI Core Modules

Module 6: Transformer Architecture from Scratch — Multi-head self-attention, positional encodings, and encoder-decoder mechanisms in PyTorch.
Module 7: Large Language Models (LLMs) — GPT, Llama, Qwen architectures, KV caching, tokenization, and quantization.
Module 8: LLM Training & Fine-Tuning — Supervised fine-tuning (SFT), LoRA rank decomposition, and QLoRA 4-bit NF4 training.
Module 9: Prompt Engineering & Structured Outputs — Chain-of-Thought, JSON Schema generation, and prompt defense.
Module 10: Vector Databases & Embeddings — Dense vector retrieval, HNSW indexing, and semantic search.
Module 11: Retrieval-Augmented Generation (RAG) — Chunking strategies, hybrid search, reranking, and hallucination reduction.
Module 13: AI Agents & Tool Calling — ReAct reasoning loops, function calling APIs, and autonomous task execution.

Interactive Practice & Production Systems

Includes hands-on browser simulators, terminal labs, code builders, and production deployment scenarios with vLLM, Ollama, and Triton inference servers.