Correct English:
“Please explain Agile methodology and its benefits. What is a sprint? Give me a step-by-step guide to apply Agile, and an example based on a real request: ‘learning AI for my class.’”
Agile is a way of delivering value in small, frequent increments instead of one big release at the end. It emphasizes collaboration, customer feedback, and the ability to adapt as you learn.
Key benefits
Faster feedback → build the right thing sooner.
Lower risk → small batches expose problems early.
Predictability → fixed-length timeboxes (sprints) create a steady rhythm.
Higher quality → continuous testing and review every iteration.
Transparency → visible backlog, demos, and progress.
Happier teams & stakeholders → shared ownership and frequent wins.
A sprint is a fixed-length iteration (commonly 1–2 weeks) with:
A Sprint Goal: one crisp outcome that guides decisions.
Ceremonies: Planning → Daily Stand-ups (15 min) → Review (demo) → Retrospective (improve).
Artifacts: Product Backlog → Sprint Backlog → Increment (the working result).
Roles: Product Owner (prioritizes value), Scrum Master (improves flow), Team (does the work).
Define the vision & success metrics (why this product/course matters; how you’ll measure success).
List user personas (students, TAs, department, you).
Create a Product Backlog of user stories: “As a <persona>, I want <capability> so that <benefit>.”
Prioritize (MoSCoW: Must/Should/Could/Won’t) by value and urgency.
Estimate effort (story points: 1, 2, 3, 5, 8…) and note risks.
Pick a sprint length (start with 2 weeks).
Sprint Planning: choose the top stories you believe you can finish; set a Sprint Goal; break stories into tasks.
Execute & inspect daily: 15-minute stand-ups to surface blockers and adjust.
Sprint Review: demo the increment to stakeholders; capture feedback.
Retrospective: identify 1–2 concrete improvements for the next sprint.
Repeat: re-prioritize the backlog based on what you learned.
Scenario: You’re preparing a 10-week “Intro to Practical AI” class. You’ll apply Agile to design & launch it iteratively.
Vision: Students can build and explain a simple ML model and use an LLM responsibly.
Success metrics: ≥80% of students complete a working mini-project; average course rating ≥4/5.
Student (beginner): wants clear, hands-on labs that run on a free setup.
Instructor (you): wants a repeatable syllabus with weekly labs and grading rubrics.
Department: wants alignment with ethics and academic integrity.
As a student, I want a one-page course map so that I know what we’ll cover each week.
As a student, I want a zero-install environment (e.g., Colab) and starter notebooks so that I can run code immediately.
As an instructor, I want a 90-minute Week-1 lesson (AI overview + hands-on) so that students get an early win.
As a student, I want a simple image-classifier lab so that I learn the ML workflow.
As a department, I want an AI ethics & academic integrity module so that policies are clear.
As an instructor, I want a mini-project brief with rubric so that grading is consistent.
As a student, I want LLM usage guidelines & prompt examples so that I use AI responsibly.
(Keep adding/backlog grooming as ideas emerge.)
Prioritize 1–4 as Must, 5–7 as Should to start.
Estimate (example): 1=2 pts, 2=5 pts, 3=5 pts, 4=8 pts, 5=3 pts, 6=5 pts, 7=3 pts.
Sprint Goal: “Enable students to run code on day 1 and deliver a concrete Week-1 experience.”
Sprint Backlog
Story 2 (5 pts): Colab-ready environment + starter notebooks.
Tasks: choose datasets; create “Hello, NumPy/Pandas/Matplotlib” notebook; test on a fresh account; write quickstart.
Story 3 (5 pts): Week-1 lesson (slides + demo).
Tasks: outline slides; embed live notebook demo; rehearse once.
Story 1 (2 pts): One-page course map.
Tasks: draft topics; add links to notebooks; export PDF.
Definition of Done (DoD)
Materials accessible via a single link, load in <30s on a school network, reviewed by 1 peer, typos <3 per document, license/attribution included.
Acceptance criteria (examples)
Story 2: A new student can open the notebook, run all cells successfully without local installs, and see expected output.
Story 3: Slides fit in 60–75 minutes with one 15-minute hands-on section; demo runs end-to-end.
Sprint calendar (example)
Day 1 (Aug 25): Planning (set Sprint Goal, pick stories).
Daily: 15-min stand-up (“Yesterday/Today/Blockers”).
Day 10–11 (Sep 3–4): Dry run; polish.
Day 12 (Sep 5): Sprint Review (demo to a colleague); gather feedback.
Day 13 (Sep 6): Retrospective; capture 2 improvements.
Day 14 (Sep 7): Publish materials; close sprint.
What you demo at Review
The live Colab notebook running successfully.
Week-1 slides with the embedded demo.
Course map PDF.
Likely Retro actions
Automate notebook environment checks.
Add a “common errors” section to the quickstart.
Story 4 (8 pts): Image-classifier lab (dataset link, step-by-step, stretch goals).
Story 5 (3 pts): Ethics & integrity mini-module (policy, examples of allowed AI use).
Story 7 (3 pts): LLM guidelines with prompt patterns and citation rules.
(Repeat the same cadence: plan → build → review → retro.)
Backlog/board: Trello/Jira/Notion (columns: To Do / In Progress / In Review / Done).
Docs: Google Docs/Slides; GitHub repo for notebooks.
Feedback: short exit ticket after each demo/lab.
“Let’s timebox this to 60 minutes.”
“Our Sprint Goal is X; does this task serve that goal?”
“Let’s slice the lab so students get a working result in the first 20 minutes.”
“Flag any blockers early so we can swarm on them.”
“We’ll inspect and adapt after the demo.”
If you want, I can turn this into a printable one-page sprint plan and a starter backlog you can paste into Trello.