AI Foundations

A hands-on introduction to how modern AI actually works : LLMs, prompting, RAG, fine-tuning, agents with live notebooks, curated videos, and a real build-your-own-agent capstone.

beginner level · 9 modules

  1. LLM & Tokenization Fundamentals
    • What is a large language model (LLM) ?
    • Tokenization explained
    • Tokenization playground
    • Embeddings and the vector space of meaning
    • Embedding similarity lab
  2. Prompt Engineering
    • What is Prompt Engineering ?
    • Zero-shot vs few-shot prompting
    • Zero-shot vs few-shot lab
    • Chain-of-thought and reasoning prompts
    • Chain-of-thought lab
    • System prompts and role conditioning
    • Prompt playground
  3. Retrieval-Augmented Generation (RAG)
    • Why LLMs hallucinate and need retrieval
    • Vector databases explained
    • Chunking strategies
    • Chunking lab
    • RAG architecture walkthrough
    • Build a mini RAG pipeline
    • Evaluating RAG quality
    • RAG evaluation lab
  4. Fine-Tuning & Model Customization
    • Fine-tuning vs RAG vs prompting
    • LoRA and PEFT explained simply
    • Fine-tuning lab
  5. LLM Evaluation, Security & Production
    • Evaluation and testing for LLM apps
    • Build a simple eval harness
    • Prompt injection and LLM security basics
    • Caching, rate limiting and cost management
    • Guardrails and safety patterns
  6. AI Agents
    • What is an agent?
    • Tools and function calling
    • TThought, action, observation — the ReAct cycle
    • Build your first agent
    • Multi-agent systems
    • Extend your agent
  7. Coding Agents & Real-World Workflow
    • What makes an agentic coding tool different from c
    • Agentic coding workflows: plan, execute, review
    • Use your first coding agent
  8. Build a RAG-powered agent from scratch
  9. Code review agent