Generative AI

You cannot use a tool well without a mental model of how it actually works.

Every GenAI app is built on top of a handful of API calls, so this is the foundational skill.

The quality of an LLM app is often decided by prompt design more than by model choice.

Embeddings are the bridge from text to search, and they underpin vector databases and RAG.

To search over many documents fast, you need a purpose-built store for vectors.

RAG lets an LLM answer accurately about your private or up-to-date data without retraining.

A stateful chat interface is the most common way users actually interact with an LLM app.

Tool-using agents let LLMs take actions and access live data, going beyond pure text generation.

You cannot trust or improve an LLM app you cannot measure and constrain.

Integrating everything into one deployed app is what turns knowledge into a portfolio piece.