AI evaluation
Model comparisons, answer-quality tests and error analysis. The outcome: a clear view of what a system can and cannot do.
↗Mateusz WięcekAI · sport · working with people
AI evaluation · Machine learning · RAG
AI you can
put to the test.
I build and test AI systems, from document retrieval to image recognition. I show what works, where a system fails and what to improve, bringing experience from sport and leading teams to the way I work.
AI + sportPrecision. Energy. People.
MSc AI candidate at Strathclyde. A background in physical education and Smile Camp. Connected by a focus on results.
About
I build and evaluate AI systems that can be checked.
I am an MSc Artificial Intelligence and Applications candidate at the University of Strathclyde. I produce evidence-based assessments of models and systems using explicit metrics, tests, error analysis, safety checks and documented assumptions.
- AI evaluation & QA
- Machine learning
- RAG & LLMs
- Computer vision
How I can help
From a question to evidence.
Looking for help with an AI project or someone to join your team? These are the problems I work on.
RAG and machine learning
Prototypes for document retrieval, classification and prediction. The outcome: a reproducible experiment with code and metrics.
↗Technology for people
I combine technical work with experience leading sports groups. I explain complex ideas clearly and turn them into practical next steps.
↗Evidence over promises
Results you can inspect.
Selected metrics from academic projects. Every number has a defined dataset, evaluation method and limitations.
How I work
Question. Experiment. Evidence.
I do not treat a model score as decoration. First I define what we are really measuring, then I build a repeatable experiment and present the result together with its limitations.
- 01A better question
- 02A reproducible test
- 03A clear conclusion
011.000Recall@3 · Clinical RAG
retrieval over a synthetic EHR dataset
Method and limitation +
How it was tested: Qrels and explicit P@3, R@3 and MRR for patient-specific retrieval.
Limit: This is not 100% answer quality or clinical effectiveness. The data is synthetic.
Project source ↗0288.62%Sign Language MNIST
aligned multiclass accuracy
Method and limitation +
How it was tested: A feed-forward network built in NumPy, with regularisation, early stopping and error analysis.
Limit: A 28×28 image benchmark; it does not measure real-world camera-based gesture recognition.
Project source ↗036.52657Spotify popularity RMSE
reported regression-model error
Method and limitation +
How it was tested: CatBoost, baselines, feature engineering, validation, diagnostic figures and tests.
Limit: The result depends on the dataset and target scale; it is not a recommender-system score.
Project source ↗047.50%GA strategy · out-of-sample
with 9.99% maximum drawdown
Method and limitation +
How it was tested: Chronological train/test evaluation, transaction costs and several comparison baselines.
Limit: A historical experiment, not a forecast of future returns or investment advice.
Project source ↗Experience
Work, projects and experiences I contribute to.
From people and communication, through demanding operations, to reproducible AI systems — each experience reinforces responsibility for the outcome.
2012—present · seasonal
Smile Camp
Camp leader & web/digital supportSince 2012, I have contributed seasonally to the Smile Camp experience, combining camp and sports leadership with web content and digital communication support.
Meet the team↗2025—2026 · expected Sep 2026
University of Strathclyde
MSc Artificial Intelligence and Applications candidateI build and evaluate ML, RAG and computer-vision systems, from clinically aware RAG over synthetic data to classification, modelling and optimisation.
View GitHub↗2019—present
Amazon DSP / Amazon Flex
Delivery Associate / DriverHigh-tempo operational work requiring routing accuracy, procedural compliance, independent judgement and effective exception handling.
More than a calling card
Explore the work, direction and person.
Four paths lead from a concise introduction to inspectable projects, current focus and publication-ready material.
Beyond the screen
Technology is only part of the story.
Sport is not an accessory to my work. Physical-education training, football, freestyle football and leading groups of young people have taught me focus, calm and responsibility for others — the same qualities I bring to technology.
Sport
Movement, focus and consistency.
People
Leading groups and creating a positive atmosphere.
Curiosity
Connecting distant fields into new ideas.
Latest thinking
Notes from the edge of technology and curiosity.
A benchmark with no peeking: double-blind AI evaluation
How a cryptographic enclave can test a closed model independently without exposing either its weights or the questions.
Read→
When an agent crosses the line: four real incidents
What Anthropic’s disclosure teaches us about isolation, permissions and monitoring for long-running AI agents.
Read→
Can AI fix its own alignment failures?
Automated researchers found mitigations for ten measurable failure classes, while still requiring supervision themselves.
Read→Start a conversation
Let’s talk about your project.
An AI project, a role, education or sport? Tell me what you are working on, your goal and the support you need. We can talk in English or Polish.
Connect on LinkedIn
Send me a message or a connection request with a little context. You can explore the projects and code before we talk.
Open LinkedIn↗Explore projects and results→