INF 385T Large Language Model Applications
“Build an AI Product in 15 weeks and submit to Y Combinator”
The University of Texas at Austin, Fall 2026
M 9:00 a.m.–12:00 p.m. · UTA 1.208
Jiaxin Pei, jiaxinpei@utexas.edu
Class Philosophy
This is not a lecture-and-exam course. It is a builder course. You will spend the semester designing, building, and shipping a real AI product—one good enough to put in front of real users.
We cover LLM fundamentals not as an end in themselves, but as tools you need to build something great. The best way to learn what LLMs can and cannot do is to push them to their limits on a problem you care about.
We welcome students with diverse backgrounds—science, engineering, design, business—as long as you are strongly motivated to build something great. We especially welcome PhD students from all areas who want to apply LLMs to their own research domains. The strongest teams combine people who can code, people who can design experiences, and people who have deep understanding of a specific domain.
We hold ourselves and our students to the highest standard. This course is fast-paced and demanding—expect to invest serious time and energy every week. But if you are passionate about building with AI and ready to put in the work, you will leave with a product you are proud of, skills that transfer directly to industry, a portfolio piece that speaks for itself—and, ultimately, an opportunity that changes your life.
The semester culminates in a public demo day with investors from top venture capital firms. Standout teams will have fast-track access to leading accelerator programs to continue building beyond the classroom.
We welcome partnerships with investors, accelerators, and industry sponsors who want to connect with the next generation of AI builders. Reach out at jiaxinpei@utexas.edu.
Syllabus
Prerequisites
Comfortable writing Python applications, using APIs, and working in a terminal. Prior experience with machine learning or natural language processing is also required.
Course materials
- Readings and resources will be provided via hyperlinks under Schedule.
- Exercises, code, etc. will be posted on Canvas.
Course overview and objectives
This course explores the design and development of practical applications powered by large language models (LLMs), including agentic systems that can plan, take actions, and use tools to complete real-world tasks. The course covers both LLM fundamentals—how LLMs work, their capabilities and limitations—and applied methods for building reliable systems, including prompting, retrieval-augmented generation, orchestration, safety, and evaluation. The course is project-driven: student teams build and iterate on an LLM-based application throughout the term, complemented by guest lectures from founders, investors, and researchers on building and assessing frontier AI systems.
By the end of this course, students will be able to:
- Design and build a working LLM-powered application from idea to deployed product
- Apply core techniques—prompting, RAG, orchestration, agentic tool use, and multi-modal integration—to solve real-world problems
- Evaluate LLM systems for quality, safety, and cost
- Iterate on a product based on real user feedback
- Communicate and present an AI product to technical and non-technical audiences
Schedule
Schedule is tentative and subject to change.
- Week 1 (8/24–8/28) – Finding ideas
- Course introduction. How to identify opportunities and find ideas for building something cool with LLMs. Guest speaker: [TBD] (30 min).
- Homework: Write four one-pagers: (1) a cool AI startup product that you like, and three AI Applications you want to build
- Week 2 (8/31–9/4) – LLM basics I
- How LLMs work; prompting and instruction-following; few-shot learning; in-context learning.
- Product to explore: [TBD]
- Guest speaker: [TBD] (30 min)
- Homework: Use AI tools to build the first demo of the LLM application you want to build
- Week 3 (9/7–9/11) (Labor Day 9/7; no class)
- Product to explore: [TBD]
- Homework: Use AI tools to build the second demo of the LLM application you want to build
- Week 4 (9/14–9/18) – LLM basics II
- Retrieval-augmented generation (RAG); embeddings and semantic search; grounding LLM outputs.
- Product to explore: [TBD]
- Guest speaker: [TBD] (30 min)
- Homework: Use AI tools to build the third demo of the LLM application you want to build
- Week 5 (9/21–9/25) – Lightweight demo day
- Students present their demos, review products others built, vote on the most promising ideas, and form teams for the class project.
- Week 6 (9/28–10/2) – Agentic systems and tool use
- Orchestration and composing LLM calls; chaining; structured outputs. Planning, multi-step reasoning, function calling, and agent frameworks.
- Product to explore: [TBD]
- Guest speaker: [TBD] (30 min)
- Homework: Build application in teams
- Week 7 (10/5–10/9) – Evaluation and testing
- Automated evals, human evals, LLM-as-judge, regression testing.
- Product to explore: [TBD]
- Guest speaker: [TBD] (30 min)
- Homework: Build application in teams
- Week 8 (10/12–10/16) – Multi-modal applications
- Vision, audio, and video models; integrating multi-modal capabilities into LLM applications; use cases and design patterns.
- Product to explore: [TBD]
- Guest speaker: [TBD] (30 min)
- Homework: Build application in teams
- Week 9 (10/19–10/23) – Safety, guardrails, and product design
- Prompt injection, hallucination mitigation, content filtering, error recovery; UX patterns, latency vs quality tradeoffs, human-in-the-loop design.
- Product to explore: [TBD]
- Guest speaker: [TBD] (30 min)
- Homework: Build application in teams
- Week 10 (10/26–10/30) – Demo day & beta test
- Teams present their applications and run beta testing with real users.
- Week 11 (11/2–11/6) – Cost optimization and deployment
- Caching, model selection, latency optimization, serving infrastructure.
- Product to explore: [TBD]
- Guest speaker: [TBD] (30 min)
- Homework: Iterate based on real user feedback
- Week 12 (11/9–11/13) – Guest lecture: Founders
- Founder perspective on building and shipping AI products. Guest speaker: [TBD] (30 min).
- Product to explore: [TBD]
- Homework: Iterate based on real user feedback
- Week 13 (11/16–11/20) – Guest lecture: Investors
- Investor perspective on evaluating AI startups, moats, and go-to-market. Guest speaker: [TBD] (30 min).
- Product to explore: [TBD]
- Homework: Iterate based on real user feedback
- Week 14 (11/23–11/27) – Thanksgiving break; no classes
- Week 15 (11/30–12/4) – Guest lecture: Researchers
- Researcher perspective on frontier AI and what’s next. Guest speaker: [TBD] (30 min).
- Product to explore: [TBD]
- Homework: Iterate based on real user feedback
- Week 16 (12/7) – Public demo day (last class day)
Course requirements and grading policy
- Individual assignments (30%) — Weeks 1–4
- Week 1: Four one-pagers on AI products
- Weeks 2–4: Three individual demos built with AI tools
- Class project (60%) — Weeks 5–16
- Teams of 3 students with complementary expertise (e.g., engineering, design, domain knowledge).
- Grading based on:
- Lightweight demo day presentation (Week 5)
- Beta test and demo day (Week 10)
- Iteration progress and user feedback incorporation (Weeks 11–15)
- Public demo day (Week 16)
- Peer review and participation (10%)
- Reviewing and voting on classmates’ demos (Week 5), beta testing other teams’ products (Week 10), and engagement with guest speakers.
- The course will use plus-minus grading, using the following scale:
| Grade | Percentage |
|---|---|
| A | ≥ 93% |
| A- | ≥ 90% |
| B+ | ≥ 87% |
| B | ≥ 83% |
| B- | ≥ 80% |
| C+ | ≥ 77% |
| C | ≥ 73% |
| C- | ≥ 70% |
| D+ | ≥ 67% |
| D | ≥ 63% |
| D- | ≥ 60% |
Academic integrity and AI tools
This course requires you to use AI tools — ChatGPT, Copilot, Cursor, and whatever else helps you build. Building on the shoulders of AI is a core skill this class teaches.
That said, using AI tools does not remove your responsibility to understand what you submit. You must be able to explain every piece of your work — how it works, why you made the choices you did, and what the tradeoffs are. If your only answer is “the AI told me to do it,” that is not your own work.
You are encouraged to discuss ideas with classmates, consult external resources, and use open-source code. However, you must cite any substantial external code or resources you incorporate. For individual assignments, the work you submit must reflect your own understanding. For team projects, all team members are expected to contribute meaningfully and be able to speak to the full scope of the project.
Notice about students with disabilities
The University of Texas at Austin provides upon request appropriate academic accommodations for qualified students with disabilities. Please contact the Division of Diversity and Community Engagement, Services for Students with Disabilities, 512-471-6259.
Notice about missed work due to religious holy days
A student who cannot meet an assignment deadline due to the observance of a religious holy day may submit the assignment up to 24 hours late without penalty, if proper notice of the planned absence has been given. Notice must be given at least 14 days prior to the due date. For religious holy days that fall within the first 2 weeks of the semester, notice should be given on the first day of the semester. Notice should be emailed to the instructor and course staff.