Build a Hybrid Search for an Enterprise RAG Knowledge Base
Overview
What this challenge is about.
Build BM25, dense, and hybrid retrievers for an enterprise RAG knowledge base, then evaluate with Recall@10, MRR@10, and nDCG@10. Get a verifiable certificate.
The scenario
The consultancy (~600 staff) deploys vertical RAG assistants under fixed-fee contracts; convincing the client steering committee is the bottleneck on every engagement.
The Brief
What you'll do, and what you'll demonstrate.
Decide whether hybrid retrieval beats dense-only and pure BM25 for an insurance-internal knowledge base, with evidence per query type.
Earning criteria — what you'll demonstrate
- Implement BM25, dense, and hybrid retrieval end-to-end
- Apply reciprocal rank fusion to merge ranked lists
- Evaluate retrievers with standard IR metrics on labeled relevance
- Slice evaluation by query type, not just aggregate
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.
- Reciprocal Rank Fusion
Apply reciprocal rank fusion to solve real industry problems and demonstrate production-level capability.
- Ir Evaluation
Apply ir evaluation to solve real industry problems and demonstrate production-level capability.
- Faiss
Apply faiss 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
Hybrid retrieval is the bread-and-butter of every RAG project; owning the implementation and evaluation is core AI-engineer work at any RAG-adjacent company.
This challenge sharpens
- hybrid-search
- bm25
- faiss
AI Solutions Architect
Defending a retrieval-design choice with sliced evaluation evidence is the AI solutions architect's daily output in enterprise engagements.
This challenge sharpens
- hybrid-search
- ir-evaluation
- reciprocal-rank-fusion
NLP Engineer
Combining classical lexical search with dense embeddings is exactly the toolkit NLP engineers ship in production search systems.
This challenge sharpens
- bm25
- dense-retrieval
- ir-evaluation