Prompting is specification. The ability to write precise specifications - for AI tools, for PRs, for tickets - is what determines output quality. The skill is the same one engineers have always needed; AI just made the feedback loop instant.
GitHub says AI is writing nearly half of all code on the platform. As that number climbs, the developer role is not disappearing - it is shifting to things AI cannot do.
How RAG works under the hood, why it solves the hallucination and knowledge cutoff problems, and what you need to build one that actually works in production.
The practical techniques that actually improve LLM output quality - structured prompts, few-shot examples, chain-of-thought, and how to build reliable AI-powered features.
How to integrate AI coding tools effectively, where they genuinely help, where they mislead you, and what you still need to understand yourself.
What is actually happening inside a large language model - tokens, attention, context windows, and why these systems behave the way they do.
