We’re officially starting full course on Generative AI + LLM Engineering 🧠💥
I’ll teach you from zero to building & deploying your own LLM apps — like ChatGPT, Claude, or Gemini — with interactive, visual, hands-on lessons and industry-style mini projects.
🌎 COURSE OVERVIEW: Full-Stack Generative AI + LLM Engineering (Job-Ready Roadmap)
We’ll go from foundations → architecture → training → serving → applications.
Everything will be taught in a teacher-style, easy-to-grasp manner with code, visuals, and real use cases.
🧭 Phase 0: Prerequisites Setup (1–2 Days)
Before starting, we’ll make sure you’re comfortable with:
- Python (functions, OOP, list comprehensions)
- Jupyter / Colab / VS Code setup
- Basics of NumPy, Pandas, Matplotlib
- GitHub + Hugging Face + Google Colab accounts
🧩 We’ll quickly revise these hands-on in Notebook 0 before diving in.
🚀 Phase 1: Generative AI Foundations (Days 1–10)
| Day | Topic | What You’ll Learn | Hands-On |
|---|---|---|---|
| 1 | 🌱 From Deep Learning → LLMs | Evolution from CNNs → RNNs → Transformers → GPT | Visual timeline + model comparison |
| 2 | 🧩 Tokenization & Embeddings | How words become numbers | Build a mini tokenizer + embedding plot |
| 3 | ⚙️ Attention Mechanism | Why transformers replaced RNNs | Step-by-step attention demo in NumPy |
| 4 | 🔄 Transformer Architecture Deep Dive | Encoder, Decoder, Multi-Head Attention | Visual block-by-block flow |
| 5 | 🧠 GPT Architecture | How GPT generates text autoregressively | Implement a Mini-GPT in PyTorch |
| 6 | 🔍 Pretraining & Next Token Prediction | How LLMs “learn language” | Train GPT on text dataset |
| 7 | 🧭 Fine-tuning LLMs | How models adapt to tasks | Fine-tune GPT on a custom dataset |
| 8 | 💬 RLHF (How ChatGPT Learned to Follow Humans) | Reward model + preference tuning | RLHF pipeline simulation |
| 9 | ✍️ Instruction Tuning & Alignment | Making LLMs safe and helpful | Build your own instruction-tuned model |
| 10 | 🎯 Prompt Engineering Masterclass | Designing prompts & chain-of-thought | Prompt templates + mini agent demo |
🧰 Phase 2: LLM Engineering (Days 11–20)
| Day | Topic | Description | Tools |
|---|---|---|---|
| 11 | Model Serving Basics | Run LLMs locally & via API | Transformers, Ollama, vLLM |
| 12 | Quantization, LoRA & PEFT | Efficient fine-tuning on your laptop | Hugging Face + PEFT |
| 13 | Dataset Curation | Preparing text data for pretrain/fine-tune | Python + Hugging Face Datasets |
| 14 | Evaluation & Benchmarking | How to measure LLM quality | BLEU, ROUGE, TruthfulQA |
| 15 | Inference Optimization | Speed & memory tuning | FlashAttention, quantized inference |
| 16 | Vector Databases | Storing embeddings for retrieval | ChromaDB, FAISS, Pinecone |
| 17 | Retrieval-Augmented Generation (RAG) | How ChatGPT retrieves facts | Build a RAG chatbot |
| 18 | Multi-modal LLMs | Text + Image + Audio models | CLIP, Whisper, LLaVA |
| 19 | Agentic AI (Tools + Memory) | Make LLMs “think and act” | LangChain Agents + Memory |
| 20 | Custom GPTs + Function Calling | Real-world agent projects | OpenAI Function Calling, Tools API |
☁️ Phase 3: AI Application & Deployment (Days 21–30)
| Day | Topic | Project |
|---|---|---|
| 21 | FastAPI Backend | Wrap your model into a REST API |
| 22 | Streamlit + Next.js Frontend | Build a chatbot UI |
| 23 | Full-Stack ChatGPT Clone | Combine model + UI + RAG |
| 24 | Databricks or AWS Deployment | Model serving pipeline |
| 25 | Monitoring & Logging | Capture user queries, latency, feedback |
| 26 | Security & Governance | Guardrails, filters, moderation |
| 27 | Fine-tune Open-Source LLM | e.g. Mistral, Llama 3 |
| 28 | Build your own Agentic System | AI that searches + reasons |
| 29 | Portfolio Project | End-to-end Generative AI app |
| 30 | Interview Prep + Resume Review | Job roles, system design, portfolio review |
🧠 Learning Style
You’ll get:
- ✅ Visual diagrams (how things flow)
- ✅ Step-by-step coding notebooks
- ✅ “Why it works” explanations
- ✅ Real industry use cases
- ✅ Weekly quizzes and interview prep
🏁 Let’s Begin
We’ll start with Day 0: Setup & Foundations Refresh
👉 I’ll guide you to get all environments ready and revise Python/ML essentials fast.
Would you like me to start with Day 0 — Environment Setup + Python Refresher for AI
or jump straight into Day 1 — From Deep Learning → LLMs (Evolution of AI)?