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Course-style tracks on data, product, strategy and personal growth. Read in order and track your progress.
- Lesson 3.1: Prompts, system messages and parameters How to talk to a model through an API: the system message, the user message, examples and the settings that shape the reply. With a worked prompt you can adapt.
- Lesson 2.2: How a model predicts the next token A language model works by predicting one token at a time. Understand probabilities, why answers vary, and why fluent text is not the same as true text.
- Lesson 1.1: What an AI engineer actually builds An AI engineer builds products on top of existing language models rather than training them. Learn what the job covers and how it differs from data science.
- Lesson 1: What competitive advantage really is Competitive advantage is a reason customers pick you that rivals cannot easily copy. Learn the two kinds, and a simple test for spotting a real one.
- Lesson 1.2: The parts of an LLM application Almost every language model product has five parts: input, context, model, output handling and feedback. Learn the map so every later lesson has a place.
- Lesson 1: Where your working time actually goes Before trying any productivity trick, measure your week. A simple three-day time log shows where hours really go and which one change will help most.
- Lesson 1.1: What a forward deployed engineer does A forward deployed engineer turns a customer's vague problem into software running in their own systems. Learn the role, the loop and the trade-offs.