beginner
intermediate
Advanced Prompt Engineering
Structured prompting, few-shot design, and reliable JSON output
AI Agents
Tool use, agent loops, and when agents beat simple prompts
AI APIs
Calling LLM APIs from code with keys, requests, and streaming
Embeddings
Vector representations, similarity search, and practical use cases
Embeddings Deep Dive
Distance metrics, normalization, chunking, and dimensionality tradeoffs
Evaluating LLM Applications
Building eval sets, metrics, and LLM-as-judge for reliable systems
Large Language Models
Transformers, tokens, context windows, and what LLMs can and cannot do
Neural Networks
Layers, weights, forward passes, and activation functions explained
Prompt Engineering
System and user messages, few-shot examples, chain-of-thought, and practical tips
RAG Basics
Retrieval-augmented generation pipeline for developer applications
Vector Databases
Storing and querying embeddings for similarity search at scale