LLM Foundations
6 Topics
1
What is AI, LLM, and Transformers?
The absolute basics: understanding Artificial Intelligence, Large Language Models, and the architecture that powers them.
Medium - Foundational knowledge expected for all engineers entering the AI space.
2
The LLM Lifecycle: Base vs Instruct Models
Understand the pipeline from raw internet text to a helpful AI assistant.
High - System design interviews strictly require knowing the difference between a Base model and an Instruct model.
3
How Transformer Models Work & KV Cache
Deep dive into KV Caching, RoPE, and core LLM architecture mechanics for system builders.
High - Expected foundational knowledge for AI engineering roles.
4
Tokenization & Embeddings
How computers represent language numerically.
Medium - Affects cost and quality
5
Fine-tuning vs. RAG
When to train your model and when to retrieve context.
High - Critical architecture decision
6
The 3 Layers of AI: From Chatbots to Agents
Understand the evolution from standard Generative AI to Code Agents and fully autonomous Agentic AI.
High - System Design interviews increasingly require distinguishing between standard LLM wrappers, ReAct agents, and Multi-Agent DAGs.