RAG Faithfulness Evaluation for a Medical-Education Assistant
Overview
What this challenge is about.
You evaluate two RAG configs with LLM-judge, NLI, and manual scoring on 200 questions to earn a verifiable certificate.
The scenario
The startup (Series A, around 30 staff) has signed letters of intent with 4 medical schools contingent on board sign-off; the eval is the last gating item.
The Brief
What you'll do, and what you'll demonstrate.
Build a multi-method faithfulness eval that lets a medical advisory board sign off on a RAG study assistant.
Earning criteria — what you'll demonstrate
- Design a multi-method faithfulness evaluation for RAG outputs
- Implement claim decomposition for fine-grained scoring
- Reason about LLM-judge bias and triangulate with non-LLM methods
- Translate evaluation results into a non-ML advisory board memo
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.
- Rag Evaluation
Apply rag evaluation to solve real industry problems and demonstrate production-level capability.
- Faithfulness
Apply faithfulness to solve real industry problems and demonstrate production-level capability.
- Llm As Judge
Apply llm as judge to solve real industry problems and demonstrate production-level capability.
- Entailment
Apply entailment to solve real industry problems and demonstrate production-level capability.
- Evaluation Harness
Apply evaluation harness to solve real industry problems and demonstrate production-level capability.
- Python
Write clean, efficient Python for data processing, automation, and backend services.
Careers
Career paths this challenge builds toward
Completing this challenge demonstrates skills that transfer directly to these roles:
AI Safety Researcher
Multi-method faithfulness evaluation with claim decomposition is exactly the eval work safety researchers do on production LLM systems.
This challenge sharpens
- faithfulness
- llm-as-judge
- evaluation-harness
AI Engineer
Standing up a reusable RAG eval harness is core AI-engineer infrastructure work in any RAG product team.
This challenge sharpens
- rag-evaluation
- evaluation-harness
- python
Applied AI Scientist
Triangulating LLM-judge with entailment and manual scoring is the kind of methodological rigor applied AI scientists bring to high-stakes deployments.
This challenge sharpens
- llm-as-judge
- entailment
- faithfulness