Run a Human-Preference Study Comparing Two Coding Assistants
Overview
What this challenge is about.
Run a Human-Preference Study Comparing Two Coding Assistants. Intermediate challenge in research. Conducting rigorous research on real questions, earn a bloc...
The Brief
What you'll do, and what you'll demonstrate.
Run a pre-registered human-preference study comparing two coding assistants and produce a vendor-decision recommendation.
This is not a research exercise. It is the work a researcher does to produce findings that withstand scrutiny. That distinction matters to every hiring manager who has seen candidates summarize papers and none who have produced original findings under expert review.
When you finish, you will have something most graduates do not: a real-world deliverable, verified by Ewance, that you can show to a hiring manager and say "I did this. Here is the proof."
Earning criteria — what you'll demonstrate
- Design a pre-registered human-preference study
- Justify sample size before collecting data
- Analyze paired-comparison data with frequentist and Bayesian methods
- Present a vendor decision under statistical uncertainty
Program Fit
Where this fits in your program.
Sharpens the same skills your degree expects you to demonstrate.
AI Measurement and Evaluation
Master · Responsible Ai
Strong alignment
This challenge maps to AI Measurement and Evaluation at the Master level. It sharpens the same practical skills your coursework expects — but in a real industry context with actual constraints and deliverables.
Skills
Skills you'll demonstrate.
Each one shows up on your verified credential.
- Experiment Design
Apply experiment design to solve real industry problems and demonstrate production-level capability.
- Statistical Evaluation
Apply statistical evaluation to solve real industry problems and demonstrate production-level capability.
- Human Evaluation
Apply human evaluation to solve real industry problems and demonstrate production-level capability.
- Pre Registration
Apply pre registration to solve real industry problems and demonstrate production-level capability.
- Llm Evaluation
Apply llm evaluation to solve real industry problems and demonstrate production-level capability.
- Stakeholder Communication
Apply stakeholder communication to solve real industry problems and demonstrate production-level capability.
Careers
Career paths this challenge builds toward
Completing this challenge demonstrates skills that transfer directly to these roles:
Applied AI Scientist
Designing a pre-registered evaluation for a real vendor decision is the applied AI scientist's contribution to product orgs.
This challenge sharpens
- experiment-design
- statistical-evaluation
- llm-evaluation
Data Scientist
Paired-comparison analysis with honest uncertainty is bread-and-butter data-scientist craft.
This challenge sharpens
- statistical-evaluation
- experiment-design
- pre-registration
AI Product Manager
Turning an evaluation into a defensible vendor decision is exactly the AI PM's contribution to the procurement conversation.
This challenge sharpens
- stakeholder-communication
- human-evaluation
- llm-evaluation