Overview
What this challenge is about.
Build a hybrid search RAG with BM25, dense embeddings, and cross-encoder reranking for an HR-policy assistant. Get a verifiable certificate.
The scenario
The startup (Series A, around 35 staff) is rolling out to 5 enterprise clients next quarter; the logistics client is the lighthouse account whose case study unlocks the rest.
The Brief
What you'll do, and what you'll demonstrate.
Build a hybrid (BM25 + dense + reranker) RAG that materially beats vector-only retrieval on an HR-policy assistant.
Earning criteria — what you'll demonstrate
- Combine sparse (BM25) and dense retrieval with rank fusion
- Apply a cross-encoder reranker to lift top-k quality
- Evaluate RAG with multiple metrics (retrieval, faithfulness, citation)
- Communicate retrieval improvements to a non-ML client
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.
- Hybrid Search
Apply hybrid search to solve real industry problems and demonstrate production-level capability.
- Bm25
Apply bm25 to solve real industry problems and demonstrate production-level capability.
- Dense Retrieval
Apply dense retrieval to solve real industry problems and demonstrate production-level capability.
- Reranking
Apply reranking 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.
- 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:
AI Engineer
Shipping hybrid retrieval with a reranker and a real eval suite is the day-one job of AI engineers at every enterprise-AI startup hiring in 2024-25.
This challenge sharpens
- hybrid-search
- dense-retrieval
- rag-evaluation
NLP Engineer
Combining BM25 with neural retrieval is the canonical NLP-engineer skill set in modern search and RAG teams.
This challenge sharpens
- bm25
- dense-retrieval
- reranking
Machine Learning Engineer
Owning a multi-metric eval and translating it into a client release is the kind of MLE follow-through that earns trust.
This challenge sharpens
- rag-evaluation
- python
- reranking