How to Build an AI Portfolio: Projects, README and Evaluation

What should an AI portfolio demonstrate?

An AI portfolio should let someone understand your problem, reproduce your work and judge the result. A polished screenshot helps, but it does not explain where the data came from, whether the evaluation is valid or what happens when an input is wrong.

Start with one complete project. This guide focuses on packaging and evaluating that project, so your portfolio shows more than a collection of tutorial notebooks.

Write a one-paragraph project brief

Use a narrow requirement such as: “Classify fictional customer support messages into billing, delivery and product questions, and flag uncertain examples for review.” State who would use the output and which actions remain manual.

Define the input and output before choosing a model. For this example, the input is a message; the output is a category and an explanation of uncertainty. If you do not have a labelled dataset, document how your sample data was created and the limits of evaluating on it.

Keep the repository easy to run

ai-ticket-classifier/
  README.md
  requirements.txt
  data/README.md
  src/train.py
  src/predict.py
  tests/sample_inputs.json
  reports/evaluation.md
  screenshots/demo.png

This is an example structure, not a required framework. The data README should explain the source, licence or permission, columns and preparation steps. Use small public or fictional examples in a shareable repository and keep credentials out of committed files.

Write setup instructions from the perspective of someone opening the project for the first time. Include the Python version you actually used, dependency installation, how to obtain the example data and the command that produces a result.

Show a baseline before an improved model

A baseline gives the result context. For a classifier, compare against a simple rule or the most frequent category. For a numeric prediction, a simple average may be a useful starting comparison. Explain why your baseline is appropriate.

Separate evaluation data before fitting preprocessing or the model. Fitting a transformation on all rows can allow information from the test set into training. Follow the scikit-learn guidance on preprocessing and data leakage when building your pipeline.

Report errors with examples

Use your measured values. Do not fill the report with invented accuracy, time savings or business results. A small project with an honest error analysis is easier to assess than an unsupported claim of near-perfect performance.

For a GenAI project, evaluate answers as well as code

If the project summarises documents or answers questions, keep a test set with expected facts or source passages. Include a question whose answer is absent, a misleading input and an unusually long example. Check whether the output follows the format and whether each factual claim is supported.

A valid JSON response can still contain a wrong answer. Report formatting failures separately from factual errors, retrieval failures and cases where the system should ask for clarification. If a model call uses a paid service, explain that requirement without committing its API key.

Use a README that answers a reviewer’s questions

1. Problem: what task does the project address?
2. Data: where did it come from, and what are its limits?
3. Setup: how can a reader run the example?
4. Approach: what baseline and model did you choose?
5. Results: what did the evaluation actually show?
6. Failures: where does it behave poorly?
7. Next step: what would you improve and how would you measure it?

Prepare a short walkthrough

Use one ordinary example and one failure case. Explain the user’s need, the important design decision and what you learned from testing. Show your own contribution if the starting point was a course exercise or public tutorial.

A useful resume statement names the work and evidence: “Built a support-message classifier, compared it with a majority-class baseline and documented misclassified examples.” Add numbers only when you measured them and can explain the method.

Choose your next skill using the AI learning roadmap and rehearse with the AI interview questions guide.