Introduction to AI Research
Jun 22–26 · Orientation + Python + AI Fundamentals · Deliverable: first pipeline run + 1-page reflection
Learning Goals
- Understand what research is and what a typical research day looks like
- Run ComplianceGPT end-to-end and see a real verdict
- Understand the 5 Python constructs you'll use all summer
- Know what AI, ML, deep learning, and LLMs are — and how they differ
- Have a working development environment
Monday, June 22 — Kickoff (Orientation Day)
What is Research?
- Research vs. coursework: the difference
- What is a research question? What is a contribution?
- What is a publication? What is a conference?
- What is a PhD and why would someone want one?
What is ComplianceGPT?
- The problem: healthcare compliance is expensive and error-prone
- The solution: AI that reads scenarios and gives verdicts
- Live demo: run the pipeline, see a PERMITTED / DENIED result
- The pipeline diagram: NL → LLM → Engine → Verdict
Complete the setup checklist. Run the pipeline on 3 rows. Write down what surprised you. Post 1 question in Slack #questions before Wednesday.
Wednesday, June 25 — Main Session: Python + AI Foundations (On campus, NCS 242)
Live coding session. Follow along — open a Jupyter notebook and type as I type.
Variables & Types
name = "hospital" count = 137 is_permitted = True
Lists & Dicts
models = ["gemma3", "llama3"]
result = {
"verdict": "PERMITTED"
}Functions
def accuracy(df):
correct = (df["v"] ==
df["gt"]).sum()
return correct / len(df)Tools: VS Code, Jupyter notebooks, GitHub. We'll commit our first notebook together.
Complete exercises 1–4 in notebooks/week1_python.ipynb. These cover: loading a CSV, filtering rows, writing a function, computing accuracy. Continue independently Thu/Fri — due Friday end of day.
Thursday–Friday, June 26–27 — Remote Work
The AI Stack
- AI — the broad field
- Machine Learning — learning from data
- Deep Learning — neural networks
- Generative AI — creating text/images/code
- LLMs — what we use in ComplianceGPT
What LLMs Can and Cannot Do
- Can: read long text, extract structure, reason step-by-step
- Cannot: guarantee factual accuracy (hallucination)
- Cannot: reason perfectly about legal edge cases
- Cannot: reliably follow complex instructions (sometimes)
Discussion: Where does AI fail?
We'll look at 3 real wrong predictions from ComplianceGPT and ask: was this an AI extraction error, a reasoning error, or is the scenario genuinely ambiguous? This question will guide all of your projects.
Week 1 Deliverable
Due: Friday, June 27 (end of day)
1. First pipeline run — Submit the CSV output from running batch_runner.py on 5 rows. The file name should be results/[yourname]_week1.csv.
2. One-page reflection — Answer these 3 questions (roughly 1 paragraph each):
- What surprised you most about ComplianceGPT today?
- Looking at your 5 results: did any surprise you? Why?
- What question about AI and compliance are you most curious about right now?
Graded on effort and engagement, not on correctness. There are no wrong answers here.
Recommended Reading This Week
- 3Blue1Brown — Neural Networks Series (YouTube) — Watch videos 1 and 2 before Wed.
- ComplianceGPT paper draft — shared via email. Skim it: just read the introduction and look at the figures.
- HIPAA Summary for Professionals — hhs.gov. Read the 2-page summary. This is the regulation we work with.