Stage 02 of 04 · Builder
AI Builders Studio
Build with LLMs, not just chat with them.
What students learn
How to build with LLMs and other AI tools — how to prompt them well, chain them, ground them with real data, and ship a small tool. Includes a serious AI ethics and safety thread.
Prompt-engineering instinct. A working API-integration pattern. The reflex to ask "who could this harm?" before shipping. Concrete experience evaluating AI output, not just trusting it.
Module-by-module
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01
What LLMs are
Token prediction, context windows, failure modes. Annotated demo session.
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02
Prompt engineering basics
Clarity, structure, examples, role prompting. 5 well-crafted prompts.
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03
Prompt engineering advanced
Chain-of-thought, structured output, evaluation. Graded portfolio.
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04
Calling an API
HTTP requests to an LLM, parse JSON. First programmatic call.
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05
Building with the API
Streamlit/Flask wrapper around LLM. First AI mini-app.
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06
Grounding & RAG (light)
Give the model real data; basic retrieval. Tool that answers questions about a real document.
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07
Safety & guardrails
Refuse, redirect, sanitize. Safety case study + a guardrail in your tool.
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08
AI ethics in the wild
Bias, privacy, environmental cost, who gets harmed. Ethics review of your own app.
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09
Final project, part 1
Pick a real user. Working v1.
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10
Final project, part 2
Test with real users, fix what's broken. v2 + reflections.
What they make
A small AI-powered tool (chatbot, summarizer, classifier, or assistant) tested with at least 3 real users.
Walk away with
Prompt-engineering instinct. A working API integration pattern. An ethics reflex.
Free resources we recommend
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π₯
Microsoft GenAI for Beginners
21 lessons, end-to-end LLM curriculum. Free GitHub Models endpoint included.
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π₯
Hugging Face Learn
Best open-weights perspective — teaches the right mental model.
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π₯
Anthropic Prompt Engineering Tutorial
9 chapters, self-grading exercises.