Overview
What this challenge is about.
Design 240 probe prompts to test an AI coder, score outputs, and write a red-team report. End with a verifiable certificate.
The scenario
The lab (~400 staff) publishes model cards as part of its release process; an external red-team report adds credibility ahead of enterprise adoption.
The Brief
What you'll do, and what you'll demonstrate.
Probe a 14B coding assistant for over-refusal, insecure code generation, and data leakage, and publish a model-card-ready red-team report.
Earning criteria — what you'll demonstrate
- Design probe prompts for distinct alignment failure modes
- Apply consistent rubrics for hand-scoring LLM outputs
- Build a severity ranking that combines likelihood and impact
- Write a public-facing red-team report
Program Fit
Where this fits in your program.
Sharpens the same skills your degree expects you to demonstrate.
Large Language Models
Master · Generative Ai
Strong alignment
This challenge maps to Large Language Models 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.
- Red Teaming
Apply red teaming to solve real industry problems and demonstrate production-level capability.
- Alignment Evaluation
Apply alignment evaluation 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.
- Prompt Design
Apply prompt design to solve real industry problems and demonstrate production-level capability.
- Risk Assessment
Apply risk assessment to solve real industry problems and demonstrate production-level capability.
- Report Writing
Apply report writing 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:
AI Safety Researcher
Designing and running an alignment red-team is the core day-to-day of safety researchers at any frontier AI lab.
This challenge sharpens
- red-teaming
- alignment-evaluation
- risk-assessment
ML Researcher
Disciplined probe design and inter-rater scoring is the methodological foundation of empirical LLM research.
This challenge sharpens
- prompt-design
- llm-evaluation
- alignment-evaluation
Research Scientist
Writing a model-card-ready red-team report is exactly the publishable output expected of a junior research scientist at an AI lab.
This challenge sharpens
- report-writing
- red-teaming
- alignment-evaluation