Part 1 Core Concepts of Data Engineering with AI
Every productive partnership starts with knowing what your partner is good at, and where it will let you down. Part 1 builds that working relationship with the AI coding companion before you point it at anything that matters.
You will start by getting set up and getting honest about what these tools can and cannot do: where an LLM accelerates real data engineering work, and where it confidently invents a column that does not exist or a library function that was never there (chapters 1 and 2). From there you put the companion to work in the two languages you live in. Chapter 3 walks through prompting techniques for SQL, from zero-shot and few-shot to chain-of-thought and beyond, using a database the model already knows well. Chapter 4 does the same for Python: calling APIs, untangling nested JSON, and generating the kind of regular expressions nobody enjoys writing by hand.
The part closes by changing how you talk to the model at all. In chapter 5 you stop pasting prompts into a chat window and start calling the OpenAI API directly from your code, turning a conversational helper into a programmable component you can build a pipeline around. By the end of Part 1 you will have the instincts, and the guardrails, to let AI move you faster without driving you off a cliff.