This course is a comprehensive, hands-on journey into Generative AI and Large Language Models (LLMs) designed specifically for Full-Stack AI Engineers. Unlike high-level or theory-only courses, this program focuses on how modern AI systems are actually built, deployed, optimized, and governed in production environments.
You will move beyond simple prompt experiments and learn how to engineer reliable, scalable, and enterprise-ready AI systems using LLMs, embeddings, retrieval, agents, tools, and full-stack application architectures. Every section of this course includes a step-by-step hands-on lab, ensuring you not only understand the concepts but also implement them in real code.
Section 1 — Introduction to Generative AI
You will build strong conceptual foundations by understanding Generative AI vs Discriminative Models, why generative systems matter, and how they are used across real-world industries such as enterprise software, healthcare, finance, and aviation.
Hands-on Lab: Compare discriminative vs generative models, generate text using transformer-based models, and map real-world generative AI use cases.
Section 2 — Transformer Architecture & LLM Fundamentals
This section demystifies how transformers actually work, including self-attention, positional encoding, and encoder vs decoder architectures. You’ll also explore tokenization, embeddings, context windows, and how LLMs are trained using pretraining, fine-tuning, instruction tuning, and RLHF.
Hands-on Lab: Implement self-attention concepts, visualize tokenization and embeddings, and simulate LLM training workflows at a high level.
Section 3 — Large Language Models in Practice
You will work hands-on with popular LLM families including GPT, Claude, Gemini, LLaMA, Mistral, and Falcon, and learn how to choose the right model based on quality, cost, latency, and use case requirements.
Hands-on Lab: Build a multi-model evaluation harness, test hallucinations and bias, and integrate LLM APIs using temperature, top-p, and max tokens.
Section 4 — Prompt Engineering for Engineers
This section teaches prompt engineering as a software engineering discipline, covering system, user, and assistant roles, zero-shot, one-shot, and few-shot prompting, and advanced techniques like chain-of-thought, self-consistency, and constraint-based prompting.
Hands-on Lab: Design robust prompt templates, defend against prompt injection, and implement input/output validation for safe prompting.
Section 5 — Embeddings & Semantic Search
You’ll learn how vector embeddings represent meaning, how cosine similarity and dot product work, and how to build semantic search pipelines using chunking strategies, embedding generation, and similarity-based retrieval.
Hands-on Lab: Build a semantic search system using FAISS and Chroma, compare chunking strategies, and evaluate retrieval accuracy.
