Building AI Products

What you will be able to do

• You will be able to decide when a language model is the right tool for a product feature and when a simpler, cheaper approach wins.
• You will be able to write production system prompts that return structured, parseable output your application can build on.
• You will be able to build a retrieval-augmented generation pipeline that grounds answers in your own data and cites its sources.
• You will be able to select chunking, embedding, and retrieval strategies (including hybrid search and reranking) that measurably improve answer quality.
• You will be able to give a model tools through function calling and design an agent loop that plans, acts, and stays inside guardrails.
• You will be able to judge when a predictable workflow beats an autonomous agent, and defend the tradeoff.
• You will be able to design an AI user experience that handles latency, uncertainty, errors, and refusals without losing the user's trust.
• You will be able to build an evaluation harness with offline eval sets, an LLM-as-judge, and regression tests that proves a change is an improvement before you ship it.

What is inside

4 modules, 8 lessons. Each module ends in a graded quiz and most carry an assignment.

1. 01

   Foundations of AI Product Engineering

   Treat a language model as a real product component: understand tokens, context windows, non-determinism, cost, and latency, then turn prompting into a versioned, testable engineering practice that produces reliable structured output. This module builds the working mental model you need before you write a single feature on top of an LLM.

   2 lessons · 5 quiz questions

2. 02

   Grounding with Retrieval-Augmented Generation

   A language model only knows what it saw during training, and that knowledge freezes the moment training ends. This module builds retrieval-augmented generation from first principles: chunking, embeddings, and vector search, then the pipeline that assembles context and cites its sources. You will learn hybrid search and reranking, the strategies that move answer quality from demo-grade to dependable.

   2 lessons · 5 quiz questions · assignment

3. 03

   Agents and Tool Use

   A model that only produces text can answer questions but cannot act. This module shows how tool use and function calling let a model call your code, how JSON schemas form the contract that keeps those calls safe, and how the agent loop lets a model plan and take multiple steps. You will also learn the judgment that separates strong teams from struggling ones: knowing when a predictable workflow beats an autonomous agent, and how to make an agent reliable when you do reach for one.

   2 lessons · 5 quiz questions

4. 04

   From Prototype to Production

   A convincing demo is not a product. This module covers the engineering and design work that makes an AI feature real: wiring the model into an application on a reliable request path, and building a user experience that survives latency, uncertainty, errors, and refusals. Then it builds the evaluation harness (offline eval sets, an LLM-as-judge, and regression tests) that lets you prove a change is an improvement before it ships.

   2 lessons · 5 quiz questions · assignment