AI ENGINEER FOUNDATIONS · STAGE 04
Deep learning & LLM foundations
Understand representations, attention, and generation.
Build a high-level mental model of neural networks, tokenization, embeddings, Transformer attention, and autoregressive generation without treating the model as a fact database.
BEFORE YOU START
Prerequisites
- Machine-learning fundamentals
- Basic vectors and probability
LEARNING OBJECTIVES
What you should be able to do
- Explain tokens and embeddings
- Describe attention conceptually
- Trace next-token generation and its limitations
WHAT YOU'LL COVER
Key concepts
SEE THE CONNECTIONS
A visual learning sequence
These are connected learning steps, not the only valid order. Revisit earlier ideas when an exercise reveals a gap.
Suggested learning sequence
Represent text
Tokenize text and map token IDs to learned vectors.
Why it matters: Neural networks process numerical representations.
Build contextual signals
Use attention and network layers to combine token information.
Why it matters: Meaning depends on surrounding context.
Generate a sequence
Predict and append tokens until a stopping condition.
Why it matters: This explains how a prompt becomes a response.
Put it into practice
Trace a short prompt through tokenization, contextual processing, and one next-token prediction, then discuss why prediction is not fact-checking.
Practice tasks
- Explain one limitation of an LLM answer.
- Sketch prompt-to-response token flow.
- Compare model knowledge with retrieved evidence.
How you'll know you're ready
- Can explain generation without claiming guaranteed understanding.
- Can identify where context windows and decoding choices matter.