PProtokol

javascript · intermediate

Modern AI Engineering

Build real, production-minded features on top of large language models: prompting, structured output, embeddings, retrieval, tool calling, evaluation, and shipping responsibly.

~10hAI Engineering10 lessons

A practical, 2026-relevant course for developers who want to build AI-powered features. You will learn what LLMs actually do under the hood, how tokens and context windows work, how to prompt reliably, how to get structured JSON output, how embeddings and similarity search work, how retrieval-augmented generation (RAG) is built, how tool/function calling lets models act on your data, how to evaluate AI output, how to manage cost/latency/limits, and how to ship AI features responsibly.

Module 01

Foundations of Language Models

What LLMs are, how they represent text, and how to prompt them well.

    Module 02

    Foundations of LLMs

    How large language models actually work under the hood.

    Module 03

    Structuring & Retrieving Knowledge

    Getting reliable structured output and grounding models in real data with embeddings, RAG, and tools.

      Module 04

      Working With AI Output

      Prompting, structured output, embeddings, and retrieval.

        Module 05

        Prompting & Structured Output

        Writing effective prompts and getting reliable structured results.

        Module 06

        Building Reliable AI Products

        Evaluating AI output and shipping AI features that are fast, affordable, and safe.

          Module 07

          Building Real AI Features

          Tool calling, evaluation, cost, and responsible shipping.

          Module 08

          Retrieval & Tools

          Grounding models in real data and letting them call functions.

          Module 09

          Shipping AI Responsibly

          Evaluating quality, managing cost and latency, and shipping safely.