Design a Continuous Eval Pipeline for an Enterprise RAG Product
Overview
What this challenge is about.
Build a continuous-eval pipeline for an enterprise RAG product, including automated scoring and a dashboard. Get a verifiable certificate.
The scenario
The startup (around 35 staff, USD 12 million Series A, 9 enterprise clients) needs the largest client's renewal to land 60 percent of next year's revenue plan; the continuous-eval commitment is the renewal's load-bearing artifact.
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.
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