Ship an MVP RAG Knowledge Assistant for a Climate-Tech Startup
Overview
What this challenge is about.
Ingest PDFs, build a vector store, and deploy a RAG web app for a climate-tech startup. Finish to earn a verifiable certificate.
The scenario
The startup (around 90 staff, 60 wind-farm operator clients across Northern Europe) wastes around 1,200 engineer hours per quarter on document lookup; even a 30 percent reduction would justify a full-time AI engineering hire.
The Brief
What you'll do, and what you'll demonstrate.
Ship a team-built MVP RAG knowledge assistant over a 4,000-document operations library with auth, logging, and an eval harness.
Earning criteria — what you'll demonstrate
- Ship a small AI product as a team using real software-engineering practice
- Design an ingestion + retrieval pipeline for mixed-format documents
- Operate a basic eval loop alongside the product
- Communicate engineering decisions to a non-engineering audience
Program Fit
Where this fits in your program.
Sharpens the same skills your degree expects you to demonstrate.
AI Software Engineering Group Project
Master · Capstone
Strong alignment
This challenge maps to AI Software Engineering Group Project 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.
- Retrieval Augmented Generation
Apply retrieval augmented generation to solve real industry problems and demonstrate production-level capability.
- Software Engineering For Ai
Apply software engineering for ai to solve real industry problems and demonstrate production-level capability.
- Vector Databases
Apply vector databases to solve real industry problems and demonstrate production-level capability.
- Python
Write clean, efficient Python for data processing, automation, and backend services.
- Ci Cd
Apply ci cd to solve real industry problems and demonstrate production-level capability.
- Team Collaboration
Apply team collaboration 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
Shipping a deployed RAG product as a team is the literal day-one job description for an AI engineer at any LLM-product startup.
This challenge sharpens
- retrieval-augmented-generation
- software-engineering-for-ai
- python
Machine Learning Engineer
Pipeline design, CI discipline, and eval-in-CI mirror the MLE's daily craft on production ML systems.
This challenge sharpens
- software-engineering-for-ai
- ci-cd
- python
AI Solutions Architect
Owning the ingestion-plus-retrieval-plus-eval design across a team is the architect's contribution at scale.
This challenge sharpens
- vector-databases
- retrieval-augmented-generation
- team-collaboration