Building AI Apps with LLMs
From first API call to a feature you can ship: prompting, structured output, tool calling, streaming, and cost control.
COURSE · AI ENGINEERING
Building AI Apps with LLMs
12 LESSONS
YOUR INSTRUCTOR
Priya Raman
Priya builds products on top of language models and, more importantly, the evaluation harnesses that tell you whether they actually work. She has shipped retrieval systems over messy, real-world corpora. Her position is that almost every bad AI feature is a retrieval or evaluation problem wearing a prompt-engineering costume.
View instructorWhat you'll learn
Understand the model
Reason about tokens, context windows, and sampling instead of treating the model as a black box.
Get structured output
Make a model return data your program can rely on, and validate it before you use it.
Wire up tool calling
Let the model call your functions, and keep the loop bounded and observable.
Ship it affordably
Stream responses, cache what repeats, and keep latency and spend under control.
Course content
4 MODULES · 12 LESSONS · 2H 27M TOTAL
MODULE 01 LLM Fundamentals
What the model is doing, what it costs, and which knobs actually change the output.
3 LESSONS · 27M
MODULE 02 Prompting That Holds Up
Writing prompts that behave the same way on the thousandth call as they did on the first.
3 LESSONS · 35M
Every lesson, searchable to the second.
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