Consolidate everything you’ve learned. Test your knowledge, practice interview questions, and build a real project.

Module 5 is designed to help you retain and apply everything you’ve learned. Instead of new concepts, you’ll find revision aids, practice materials, and a hands-on project that ties the entire phase together.

After completing this module, you will be able to:

• ✅ Recall all key concepts from Phase 4 from memory
• ✅ Answer interview questions about LLMs
• ✅ Score 80%+ on phase MCQs
• ✅ Build a functional LLM-powered application

mindmap
  root((Phase 4:<br/>Large Language<br/>Models))
    Module 1: Foundations
      What is an LLM
      Language Models
      Tokenization
      Context Window
    Module 2: Architecture
      Transformer
      Self-Attention
      QKV
      Multi-Head Attention
      Positional Encoding
      Feed-Forward Network
      Decoder-Only
      GPT Architecture
    Module 3: Training
      Pretraining
      Next Token Prediction
      Supervised Fine-Tuning
      RLHF
      DPO
    Module 4: Inference
      Inference Pipeline
      Decoding Strategies
      Temperature / Top-K / Top-P
      Streaming
      Function Calling
      Structured Output
      Hallucinations

Before moving on, you should be able to answer:

1. How does an LLM generate text from a prompt?
2. What are the key components of a Transformer block?
3. How does RLHF align LLM outputs with human preferences?
4. What causes hallucinations and how do you mitigate them?

➡️ After completing this module, you’re ready for Phase 5: Retrieval-Augmented Generation (RAG) — coming soon.