Overview
What this challenge is about.
Index around 5,000 internal-knowledge docs across the three languages using a multilingual embedding model (e.g., multilingual-e5 or BGE-M3). Build the retrieval-then-answer pipeline with a small open chat LLM (Mistral, Qwen2, or similar). Handle language-detection for query, route citations to original-language snippets, and respond in asker's language. Construct a 60-question multilingual eval set; report retrieval accuracy (Recall@5), answer correctness (LLM-as-judge + spot human review), and citation precision. Write a 5-page deployment plan for the internal-ops team.
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.
Skills
Skills you'll demonstrate.
Each one shows up on your verified credential.
Careers
Roles this prepares you for.
Real titles. Real skill bridges. Pick the one closest to your trajectory.
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