Learn the basic architecture of AI systems — model, prompt, context, and feedback — in a way that makes the workflow understandable.

Practice the prompt loop that drives reliable AI output — define the goal, write the instruction, review the answer, and refine.

Learn the core vocabulary and workflow of machine learning — supervised vs unsupervised learning, features and labels, train/test splits, and the overfitting trap.

Understand how neural networks learn — neurons, weights, activation functions, the forward pass, loss, backpropagation, and gradient descent.

Understand how large language models actually work — tokens, embeddings, self-attention, context windows, and autoregressive generation.

Learn how RAG grounds LLM answers in your own documents — chunking, embeddings, vector retrieval, ranking, and cited generation.

Learn how trained models become fast, reliable production endpoints — batching, quantization, GPU inference, latency budgets, and autoscaling.

Learn the operational discipline that keeps ML systems correct in production — experiment tracking, data pipelines, evaluation, drift, and CI/CD for models.

Learn how vector databases serve millions of embeddings — ANN indexes, HNSW and IVF, cosine similarity, and hybrid search.

Learn what it takes to run LLM features in production — evals, prompt injection defenses, caching, cost control, observability, and governance.