Learn AI by Building Systems

Learn how Large Language Models work from the ground up: tokens, context windows, hyperparameters, prompts, structured outputs, embeddings, semantic search, evaluation, and attention mechanics.

Deconstruct deep learning layers, weights, biases, backpropagation, and activation/loss functions from scratch.

Understand recurrent memory architectures, LSTMs, and gated structures preceding modern attention.

Deep dive into Attention Is All You Need. Study positional encoding, multi-head attention, and decoder layers.

Master vector representation spacing, similarity indexing algorithms, and hybrid metadata keyword searches.

Build robust retrieval-augmented pipelines integrating document loaders, rerankers, and retrieval evaluations.

Build predictable business workflows enforcing structured JSON schema extractions and intents routing.

Design autonomous loops combining function call registrations, multi-step planners, and reflection cycles.

Establish Model Context Protocol clients, host secure custom servers, and define execution sandboxes.

Orchestrate supervisor hierarchies, agent conversations protocols, and human-in-the-loop validation gates.

Scale model routing hubs. Integrate semantic caches, prompt registries, cost limits, and latency fallbacks.

Run models in production. Master Server-Sent Events streams, inputs guardrails, and automated evaluation pipelines.