LLM: From Basics to Practical Use

A practical course on large language models (LLMs) — from how they’re built to assembling real applications. Aimed at a developer who wants not to “play with a chatbot” but to deliberately embed LLMs into products: to understand what a model can and fundamentally cannot do, write reliable prompts, work with APIs, build embedding-based search and RAG, connect tools and agents, evaluate quality, and ship it all to production.

The course is not about the mathematics of transformers or about training models from scratch — it’s about engineering on top of ready-made models. Being able to program is enough; the examples are in Python (the lingua franca of the LLM ecosystem), but all communication with models goes over HTTP, so the approach applies on any stack.

The key principle running through the whole course: LLMs are non-deterministic and don’t know the truth — they predict plausible text. So engineering around them isn’t “asking the model” but building a system: give it the right context, constrain the format, check the output, measure quality, and defend against failures and abuse. The model is a powerful but unreliable component; the engineer’s job is to make the system reliable around an unreliable component.

Pace 1–2 hours a day. Each lesson contains theory, code examples, a checklist of practical tasks, and a quiz. The model landscape changes monthly, so specific names and prices are given as an illustration as of mid-2026 — the principles of choosing matter more than the current benchmark champion.