Design a Continuous Eval Pipeline for an Enterprise RAG Product
Overview
What this challenge is about.
Design a Continuous Eval Pipeline for an Enterprise RAG Product. Advanced challenge in design. Designing real products under real constraints, earn a blockch...
The Brief
What you'll do, and what you'll demonstrate.
Design and build a working slice of a continuous-eval pipeline for an enterprise RAG product, plus a customer-facing commitment document.
This is not a design exercise. It is the work a product designer does between a brief and a shipped interface. That distinction matters to every hiring manager who has seen candidates redesign Spotify's homepage and none who have worked under real product 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 eval set with realistic query-class coverage for RAG
- Combine LLM-as-judge with deterministic checks for honest scoring
- Build a continuous-eval pipeline architecture
- Translate eval commitments into customer-facing prose
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.
- Continuous Evaluation
Apply continuous 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.
- Retrieval Augmented Generation
Apply retrieval augmented generation to solve real industry problems and demonstrate production-level capability.
- Python
Write clean, efficient Python for data processing, automation, and backend services.
- Mlops Design
Apply mlops design 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:
AI Engineer
Building the working slice end-to-end is the AI engineer's bread and butter at any RAG-shipping team.
This challenge sharpens
- retrieval-augmented-generation
- python
- llm-evaluation
Prompt Engineer
LLM-as-judge prompt design with validation is exactly the prompt engineer's contribution to a serious eval pipeline.
This challenge sharpens
- llm-evaluation
- continuous-evaluation
- stakeholder-communication