Assignments & project

Implementation assignments

Three short, hands-on assignments — 15% of the final grade between them. They are about getting real systems running end to end, not about squeezing out the last point of accuracy.

Late policy: 10% penalty per 24 hours (or fraction thereof) up to 72 hours, after which a zero is recorded without an approved extension.

Assignment 1

Topic and handout to be announced. Expect a build-and-measure exercise on the foundations material from the first third of the term.

Assignment 2

Topic and handout to be announced. Expect a build-and-measure exercise on the systems and agent material from the middle of the term.

Assignment 3

Topic and handout to be announced. Expect a build-and-measure exercise on the agent-infrastructure or applications material from the later part of the term.

Research project

The project is 50% of the final grade and runs the length of the semester. It is the main deliverable of the course, and it should read like a workshop or conference submission by the end.

Teams

Teams of up to three students. Solo projects are permitted but held to the same standard of scope, so a team is usually the better choice. Conceptual discussion across teams is encouraged; code sharing across teams is not permitted.

What makes a good project

Pick something you could plausibly finish and measure in a semester. The strongest projects share a shape:

1. A specific claim about LLM or agentic systems that is not yet settled in the literature.
2. An experiment that could falsify it.
3. A baseline you can actually run, and a metric you can defend.

Projects may be systems-flavored (serving, retrieval, caching, orchestration), evaluation-flavored (benchmarks, judge reliability, contamination), or application-flavored (a domain deployment with real constraints). Reproduction studies of a recent paper are welcome when paired with an extension of your own.

Deliverables

There are three graded milestones. Together they make up the 50%:

Individual due dates will be announced early in the term.

Proposal — 10%

A short document (2–3 pages) covering:

• The problem and why it matters now.
• Closely related work, and precisely what you would add to it.
• Planned method and the experiments you intend to run.
• How you will evaluate success — metrics, baselines, datasets.
• A week-by-week plan and a division of labor across the team.

Midterm report & presentation — 15%

• Report (10%): progress against the proposal — what works, what you have measured so far, what turned out to be harder than expected, and a revised plan for the remainder of the term. Negative results reported honestly are worth more than optimistic projections.
• Presentation (5%): a short in-class presentation of the same material, so the room can push back on the plan while there is still time to change it. The meeting it occupies will be announced.

Final report & presentation — 25%

• Report (15%): a conference-style write-up — motivation, method, experimental setup, results, limitations, related work — together with your code and instructions sufficient for someone else to reproduce the headline result.
• Presentation (10%): in class during week 15 (2026-12-07).
• AI-use disclosure: if you used AI coding assistants, include a short appendix listing the models and tools, representative prompts, and where in the work they were applied. This is required, and it is not held against you — undisclosed use is.

Grading emphasis

Projects are judged on the quality of the question, the rigor of the evaluation, and the honesty of the analysis — not on whether the result was positive. A carefully executed project that refutes its own hypothesis scores well. A project with impressive numbers and an unfalsifiable setup does not.

Submission

All work is submitted on Blackboard unless stated otherwise. Deadlines are 23:59 Saudi time (AST) on the stated date.