Ship an MVP RAG Knowledge Assistant for a Climate-Tech Startup
Overview
What this challenge is about.
Ship an MVP RAG Knowledge Assistant for a Climate-Tech Startup. Advanced challenge in code. Writing production code that solves real engineering problems, ea...
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.
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
- 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