How to write an AI project case study

A reader opens your portfolio and sees “Built an AI assistant with RAG.” They still do not know what it does, which parts you built, or whether you checked the answers.

A case study fills in those gaps. We'll use a small document assistant as an example, but the same structure works for a prompt workflow or voice agent.

1. State the problem in one sentence

Name the person, the task, and the result. Instead of “AI-powered customer support,” write: “A shop customer can ask a returns question and see the policy paragraph supporting the answer.”

This gives the reader a way to judge the project. If your interface answers unrelated questions but cannot identify the returns exception, it has missed the stated task.

2. Describe the version you actually built

List what is included and what is outside scope. A first version might support one text document, typed questions, and source citations. It might not support scanned PDFs, telephone calls, or refunds.

State whether this is a learning project, a prototype, or a deployed application. A small scope is not a weakness when it is explicit. Avoid inventing customers, usage figures, or commercial outcomes.

3. Explain your own contribution

If you followed a tutorial or started from a template, link to it. Explain what you added or changed.

Separate provider capabilities from application work. The language model generates text; you may have written the retrieval logic, source display, permission checks, and tests. A voice provider may synthesize speech; you may have built the turn controls and recovery behavior.

This makes the project easier to discuss because a reviewer can see which decisions and code belong to your work.

4. Show the flow through the system

Walk one question from input to output. For the document assistant, explain how the question becomes a search query, how passages are selected, what reaches the model, and how citations appear in the interface.

Use the same example throughout. “Can I return a personalized notebook?” is more informative than switching between unrelated weather, finance, and travel examples in each section.

An architecture explanation should identify responsibilities, not just name libraries. Say where credentials live and where the application decides what the user may access.

5. Explain one real trade-off

A trade-off is a choice between alternatives with different costs or limitations. For example, keyword retrieval is easy to inspect but may miss alternate wording. Adding semantic search changes the implementation and should be evaluated against the same questions.

Describe what you chose and why it suited this version. Then state what evidence would make you reconsider. Do not present a choice as universally best when it was made for a small demonstration.

6. Include evidence from tests you ran

Show the input, expected behavior, actual result, and interpretation. Include a failure if one taught you something.

Question: Can I return a personalized notebook?
Expected source: the personalized-item exception
Observed result: fill this in after running your test
What changed: describe the actual revision, if any

This is a reporting template, not a completed test. Replace the placeholders with your own observations before publishing it as evidence.

Keep illustrative examples clearly separate from measured results. If you report an accuracy, latency, or cost figure, explain how you obtained it and which setup it describes.

7. Make the project inspectable

Provide a repository or demo link where appropriate, a small shareable input, and setup instructions. Name required environment variables without revealing their values.

If the application uses paid services, identify which parts require them. If there is no hosted demo, say that and explain how to run it locally. Remove broken buttons rather than leaving the reader to guess whether the project is available.

Use fictional or permitted data. Private customer transcripts and documents are not suitable portfolio evidence merely because they would make the story feel more realistic.

8. Write the limitations and next experiment

Tie limitations to behavior. “Needs improvement” is vague. “The keyword baseline misses questions that use synonyms” identifies something you can investigate.

Choose a next experiment that addresses a known gap. Keep the original test cases so you can compare the next version with this one.

You do not need to promise a large roadmap. A project with one clear purpose, a working path, and an honest account of its limitations is easier to assess than a feature list with no evidence.

9. Assemble a readable first draft

Use this order:

• Problem: who needs what result?
• Scope: what does this version include?
• Contribution: what did you build or change?
• Implementation: how does one request move through the system?
• Decision: which trade-off mattered?
• Evidence: what did you test and observe?
• Limitations: where does the result fall short?
• Reproduction: how can another person inspect or try it?

Keep the length proportional to the work. A concise case study with specific evidence can be more useful than a long article repeating general AI concepts.

Choose a starting point from the project directory. If you want to connect the work to a learning direction, compare it with the AI career roadmaps. Role expectations vary; the case study should explain your skills rather than promise a hiring outcome.