Overview
What this challenge is about.
You will index 5,000 multilingual docs, build a retrieval-augmented QA pipeline with an open LLM, and evaluate accuracy. Get a verifiable certificate.
The scenario
The Milan retailer's AI team (around 35 engineers inside a 12,000-person retailer) sees multilingual internal RAG as the productivity unlock for its 4,000-employee corporate functions.
The Brief
What you'll do, and what you'll demonstrate.
Build a cross-lingual RAG QA system that retrieves across 3 languages and answers in the asker's language with citations.
Earning criteria — what you'll demonstrate
- Apply multilingual embeddings to a real cross-lingual retrieval problem
- Build a citation-bearing RAG pipeline
- Evaluate RAG with separate retrieval and answer metrics
- Plan a deployment for a multilingual internal-tools audience
Program Fit
Where this fits in your program.
Sharpens the same skills your degree expects you to demonstrate.
Aligned coursework coming soon.
Skills
Skills you'll demonstrate.
Each one shows up on your verified credential.
- Rag
Apply rag to solve real industry problems and demonstrate production-level capability.
- Cross Lingual Retrieval
Apply cross lingual retrieval to solve real industry problems and demonstrate production-level capability.
- Multilingual Embeddings
Apply multilingual embeddings to solve real industry problems and demonstrate production-level capability.
- Llm Tool Use
Apply llm tool use to solve real industry problems and demonstrate production-level capability.
- Evaluation
Apply evaluation to solve real industry problems and demonstrate production-level capability.
- Pytorch
Apply pytorch 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 cross-lingual RAG with citations and language routing is the AI-engineer work enterprise AI teams urgently need as they expand internal AI to non-English-first orgs.
This challenge sharpens
- rag
- cross-lingual-retrieval
- multilingual-embeddings
NLP Engineer
Multilingual embedding tuning and cross-lingual retrieval evaluation is core NLP-engineer work at any global enterprise rolling out RAG.
This challenge sharpens
- cross-lingual-retrieval
- multilingual-embeddings
- evaluation
AI Solutions Architect
Writing the deployment plan that operations can adopt is the AI solutions architecture work consultancies sell to multilingual enterprises.
This challenge sharpens
- rag
- llm-tool-use
- evaluation