Overview
What this challenge is about.
Build an Evaluation Harness for an Internal LLM Assistant. Advanced challenge in code. Writing production code that solves real engineering problems, earn a ...
The Brief
What you'll do, and what you'll demonstrate.
Build a reusable LLM evaluation harness that covers helpfulness, grounding, refusal, and prompt-injection resistance, and use it to pick a base model.
This is not a coding exercise. It is the work a software engineer does between a Jira ticket and a merged PR. That distinction matters to every hiring manager who has seen candidates solve LeetCode problems and none who have shipped production code under real constraints.
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 an evaluation harness that covers safety and quality dimensions
- Apply LLM-as-judge with rubrics and inter-rater calibration
- Test for prompt injection with a meaningful threat model
- Communicate evaluation results as a model-selection decision
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.
- Llm Evaluation
Apply llm evaluation 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.
- Prompt Injection Testing
Apply prompt injection testing to solve real industry problems and demonstrate production-level capability.
- Grounding Evaluation
Apply grounding evaluation 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.
- 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
Building a multi-dimensional LLM evaluation harness is core safety-research work at any enterprise-AI vendor.
This challenge sharpens
- llm-evaluation
- prompt-injection-testing
- grounding-evaluation
ML Researcher
Designing test cases and judge calibration is the methodological core of LLM-as-judge research.
This challenge sharpens
- llm-as-judge
- benchmark-design
- llm-evaluation
AI Engineer
Wiring a reusable evaluation harness into the engagement workflow is the AI-engineer skillset that consultancies hire for.
This challenge sharpens
- python
- llm-evaluation
- benchmark-design