Long-Context QA Evaluation Benchmark for Legal Memoranda
Overview
What this challenge is about.
Build a benchmark testing 3 LLMs on legal memoranda QA, score F1 by answer position, and get a verifiable certificate.
The scenario
The startup (Series A, around 30 staff) is evaluating models for a contract worth roughly USD 1.2M annually in inference spend; the wrong choice would burn months of margin.
The Brief
What you'll do, and what you'll demonstrate.
Design a long-context QA benchmark that exposes position-dependent accuracy and recommend one model variant for production.
Earning criteria — what you'll demonstrate
- Design a long-context evaluation that captures position-dependent effects
- Apply strict F1 evaluation on long-document span extraction
- Reason about 'lost in the middle' in modern long-context LLMs
- Translate model-benchmark findings into a procurement recommendation
Program Fit
Where this fits in your program.
Sharpens the same skills your degree expects you to demonstrate.
Aligned coursework coming soon.
Skills
Skills you'll demonstrate.
Each one shows up on your verified credential.
- Long Context Qa
Apply long context qa to solve real industry problems and demonstrate production-level capability.
- Benchmark Design
Apply benchmark design to solve real industry problems and demonstrate production-level capability.
- Model Evaluation
Apply model evaluation to solve real industry problems and demonstrate production-level capability.
- Lost In The Middle
Apply lost in the middle to solve real industry problems and demonstrate production-level capability.
- Reading Comprehension
Apply reading comprehension to solve real industry problems and demonstrate production-level capability.
- Experiment Design
Apply experiment design 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:
ML Researcher
Designing a long-context benchmark that exposes position effects is genuine applied-research work and a strong interview portfolio piece.
This challenge sharpens
- benchmark-design
- long-context-qa
- experiment-design
Applied AI Scientist
Turning a model benchmark into a procurement recommendation for a CTO is exactly what applied AI scientists do at vertical AI startups.
This challenge sharpens
- model-evaluation
- long-context-qa
- experiment-design
AI Safety Researcher
Surfacing capability-failure modes like 'lost in the middle' is the kind of evaluation work safety researchers do on long-context systems.
This challenge sharpens
- lost-in-the-middle
- model-evaluation
- benchmark-design