Building with AI
How to structure an AI project, navigate the tools landscape (RAG, agents, vector databases, APIs), write basic code, and evaluate outputs against cost and latency.
Everyone talks about building with AI. Most people are still stuck admiring the demo. This block moves you from spectator to builder, and it does it without pretending you need a computer science degree to have an opinion that matters in the room.
You start where every serious project should start, before a single line of code. Framing the problem. Deciding whether you need a prompt, a RAG setup, a fine-tune, or an agent. Setting scope, data, and success criteria so you know what winning looks like. This is the discipline that separates real initiatives from expensive science experiments.
Then you go into the machinery. You map the tools landscape, from APIs to no-code platforms to vector databases. You learn how retrieval-augmented generation puts your own data behind the model, so it stops guessing and starts answering. You see how agents take actions instead of just producing text, and where that power gets dangerous if nobody scoped it properly.
Finally, you get your hands dirty. Your first API call in Python, so you understand what your teams actually do. Real evaluation, because a system that feels smart and a system that works are two different things. And the tradeoffs that decide budgets, cost, latency, and model selection, the choices that quietly make or break your unit economics.
By the end you can sit across from any technical team and hold the conversation as a peer. You will know what is easy, what is hard, what is a red flag, and what is a excuse. That is leverage. Leaders who understand how AI gets built stop being sold to and start directing the work.
What you'll master
- Frame an AI problem before anyone touches a keyboard
- Choose between prompting, RAG, fine-tuning, and agents with confidence
- Scope a project with clear data needs and success criteria
- Design a RAG system that puts your own data behind the model
- Evaluate outputs to prove a system actually works
- Weigh cost, latency, and model tradeoffs like an operator
- Hold a peer-level conversation with any technical team
Key terms
Modules
How to frame an AI problem, pick an approach, and define scope, data, and success criteria.
An overview of AI tooling plus how RAG and agents extend models with data and actions.
Making your first API call and evaluating outputs against quality, cost, and latency.