Multilingual RAG for a European Customer-Support Knowledge Base
Overview
What this challenge is about.
Build a multilingual RAG for an automotive knowledge base using 6,000 documents in 4 languages. Retrieve and generate answers. Get a verifiable certificate.
The scenario
The supplier (around 1,400 staff, EUR 280M revenue) has 380 support agents across 6 European sites; the RAG launch is contractually tied to a 15 percent reduction in mean time to resolution.
The Brief
What you'll do, and what you'll demonstrate.
Build a multilingual RAG that retrieves across 4 languages and answers in the query language with original-language citations, meeting per-language quality bars.
Earning criteria — what you'll demonstrate
- Apply multilingual embedding models to cross-lingual retrieval
- Design per-language evaluation that surfaces minority-language weaknesses
- Manage citation hand-off between source language and answer language
- Communicate readiness to an enterprise customer's leadership
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.
- Multilingual Rag
Apply multilingual 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.
- Rag Evaluation
Apply rag evaluation to solve real industry problems and demonstrate production-level capability.
- Citation Handling
Apply citation handling to solve real industry problems and demonstrate production-level capability.
- Python
Write clean, efficient Python for data processing, automation, and backend services.
Careers
Career paths this challenge builds toward
Completing this challenge demonstrates skills that transfer directly to these roles:
NLP Engineer
Multilingual RAG with cross-lingual retrieval and per-language evaluation is core NLP-engineer work in any global enterprise AI deployment.
This challenge sharpens
- multilingual-rag
- cross-lingual-retrieval
- multilingual-embeddings
AI Solutions Architect
Translating enterprise multilingual requirements into a pipeline architecture plus a launch-readiness memo is exactly the AI-solutions-architect skill set.
This challenge sharpens
- multilingual-rag
- citation-handling
- rag-evaluation
AI Engineer
Wiring multilingual embeddings + reranker + multilingual generation into a customer-deployable pipeline is core AI-engineer work.
This challenge sharpens
- multilingual-rag
- python
- citation-handling