Genai

Why ‘just ask for JSON’ isn’t enough, and the real techniques — schemas, function calling, output parsers — for getting consistent, parseable output every time.

The named prompt engineering patterns practitioners actually use — chain-of-thought, role prompting, and self-consistency — explained simply.

Moving from one-off prompts scattered in code to versioned, parameterized templates you can actually test, reuse, and maintain.

GenAI systems need real evaluation — accuracy, relevance, safety — just like any other engineering system, not vibes-based judgment.

The shift from a model that answers questions to one that takes real actions using tools — the plan, act, observe, repeat loop.

The honest explanation for why LLMs make things up, and practical ways to reduce it — grounding, citations, and a few other levers.

How text gets turned into numbers that capture meaning, and why ‘similar meaning = nearby numbers’ is the foundation of search, recommendations, and RAG.

Why specificity beats cleverness, and how to talk to a model that takes everything you say literally.

What a token actually is, why every model has a memory limit, and why this quietly affects cost, speed, and what you can even ask a model to do.

No math-heavy transformer internals, just the core idea: an LLM is a very sophisticated autocomplete engine trained on huge amounts of text.