Title: AIB Capstone
Course: AI Guide Academy > Section: AI-Basics (AIB)

AI Basics Capstone Project

Please hand in your capstone project here! A few more details are in the video below, or if you prefer, you can read the transcript under the video.

Capstone Video

AIB Capstone Project - Instructions Transcript

Now it's your turn. I'd like you to  create a workflow  for yourself to help identify how and when you want to use generative AI. This workflow should:

- Include everything we've discussed so far.
- Guide you through a  decision-making process  to determine whether or not you should use a large language model (LLM) tool.
- Be applied in  at least three small use cases .
- Projects can be submitted in English or German.

Key Elements to Include in Your Workflow

You'll need to come up with your own guiding questions, but here are some prompts to help you get started:

1. Capability Assessment :
  - Can the AI tool actually fulfill your task?
  - In this task, what should be done by a human, and what can be delegated to a machine?
  - How do you separate the responsibilities between humans and machines?
2. Limitations & Requirements :
  - Are there limits to the AI's ability that you'll need to work around?
  - Do you need to provide the tool with extra  knowledge or input ?
3. Benchmarking Expectations :
  - What are your expectations for the tool?
  - Do you see the AI as a  trainee  to guide, or as a  guide  to help you learn something new?
  - What exactly do you expect it to do for you?
4. Evaluation & Comparison :
  - If you're benchmarking yourself against the tool, how do you ensure you're giving it the best chance to succeed?
  - Do you need any  additional steps before or after  using the tool to make a fair comparison?
  - What are you comparing the tool against—yourself, or another tool?
5. Ethical & Legal Considerations :
  - Are there  boundaries you don't want to cross ?
  - Could your task involve legal or ethical issues?
  - Think ahead and identify potential problems.

Deliverable

With these questions in mind,  create a document, checklist, or tool, whatever format works best for you, that outlines your workflow. The goal is to help you reach a  clear conclusion :

- Is the tool  worth using ?
- Or would it be  more effective  to complete the task manually?

Final Instructions

- Have your workflow ready.
- Use it three times .
- Try at least  two different tools, or one tool compared to your own process.
- Make  informed decisions  about which AI tools you want (or don't want) to use.

This is a  big project , and it will take time. But I truly believe it's worthwhile, and I hope you enjoy the process. Have fun!

Grading form:

AIB Capstone Project Rubric

Workflow Completeness & Clarity

- Workflow is missing or mostly off-topic.
- Workflow is vague, incomplete, or missing key reflective elements.
- Workflow includes basic steps with some reasoning. Lacks depth or personalization.
- Workflow is well-structured and includes most reflection points. Minor gaps or unclear steps.
- Workflow is detailed and integrates all core reflection questions (e.g. capability, limits, benchmarking, ethics). Steps are logical and practical.
Reflection Questions & Decision Criteria

- No meaningful reflection questions or decision criteria provided.
- Reflection is minimal or unclear. Criteria are poorly aligned with AI evaluation.
- Basic reflection present, but questions are too general or underdeveloped.
- Good set of questions that reflect thoughtful evaluation of AI use. Minor lack in clarity or precision.
- Clearly defined decision questions show deep critical thinking (e.g. when to use LLMs, task delegation, ethical limits). Questions are actionable.
Execution of Use Cases (3 examples, 2 tools)

- No clear use case application or testing of workflow.
- 1–2 vague examples with little documentation or no clear testing.
- 2–3 examples attempted, but comparison or documentation is inconsistent.
- 3 use cases completed with 2 tools, with mostly clear documentation and comparison.
- All 3 use cases executed using 2 tools (or comparison with self). Documentation is detailed and thoughtful.
Critical Benchmarking & Comparison

- No benchmarking or critical evaluation attempted.
- Weak or unclear comparison; lacks objectivity.
- Basic comparison present, but lacks structure or justification.
- Comparison is solid, with some benchmarks or thoughtful contrast of tools.
- Benchmarking, clear metrics, and fair comparisons (e.g. speed, accuracy, effort). Uses self or alternative tool wisely.
Conclusion & Decision-Making

- No conclusion or entirely unsupported claims.
- Weak or unclear conclusion; little connection to earlier steps.
- Conclusion is present, but only loosely based on testing/reflection.
- Decision is logical and mostly supported by workflow and results.
- Final decision is well-reasoned and strongly supported by testing results, workflow logic, and self-reflection.