Understanding Large Language Models
Go beyond the surface. Learn how transformers work, why models hallucinate, how context windows function, and what model architecture choices mean for your applications.
About This Course
Most developers use LLMs as black boxes. The best AI engineers understand what's happening inside — not to implement transformers from scratch, but to make better architectural decisions, debug weird model behavior, and predict how models will perform on novel tasks. This course builds your intuition for LLMs through clear explanations, visualizations, and experiments — no PhD required. When we cover transformer architecture, attention, and embeddings, we use plain-English analogies and diagrams — no calculus, no matrix math beyond a simple dot product explained from scratch.
What You'll Learn
- Understand transformer architecture intuitively — attention, layers, embeddings
- Explain why LLMs hallucinate and how to mitigate it in production
- Make informed choices between GPT-4, Claude, Gemini, Llama based on task requirements
- Understand context window limits and how to work within them effectively
- Read and interpret AI benchmarks to evaluate model capability claims
- Understand embeddings and vector representations conceptually
- Know when fine-tuning vs. prompting vs. RAG is the right solution
- Assess the safety and alignment properties of different models
Who Is This For?
Want to make informed architecture decisions instead of just guessing which model to use
Need to evaluate AI vendors, understand tradeoffs, and guide engineering teams without being experts themselves
Want to understand the technology behind the AI revolution at a deeper level than news articles provide
Prerequisites
- Basic Python (or take Python for AI first)