Applied AI

A plain-English guide to Claude Code and Cowork: the .claude folder, CLAUDE.md, skills, slash commands, hooks, subagents, and MCP — from your first install to genuine mastery.

From never typing 'git' to understanding Git's internals, GitHub collaboration, and how GitHub Copilot's skills, custom instructions, context window, and memory actually work.

Stop guessing at prompts. Learn how LLMs read your words, then how to write prompts that work — from anatomy and system prompts to tool calling, chaining, evals, and safety.

Go from 'I am not sure what an AI model even is' to confidently designing RAG systems — every term explained in plain English, built around a single librarian analogy.

How vector databases turn meaning into math and find it fast — embeddings, similarity, ANN indexes, quantization, hybrid search, and running them in production.

Build AI agents from first principles — the think-act-observe loop, tools, memory, planning, reflection, and how to keep them safe — starting from 'what even is an agent?'

Understand the Model Context Protocol end to end — from JSON-RPC messages to a secure, multi-tenant MCP platform — explained for someone seeing it for the first time.

Take an AI system from a working demo to a reliable, safe, affordable, well-loved product — observability, tracing, evaluation, cost control, security, human-in-the-loop, and deployment.

Design, build, optimize, and operate production-grade systems that serve LLMs — GPUs, the transformer, model serving, quantization, inference optimization, vLLM, and multi-GPU scaling.

Teach an LLM new skills, knowledge, and behavior — from 'what is a model?' to LoRA, QLoRA, synthetic data, preference tuning, evaluation, and shipping a fine-tuned model like a pro.