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Build an Embedding-Based Semantic Search for a Legal-Document Corpus

FreeVerified credential3 weeksIntermediate

Overview

What this challenge is about.

Build a semantic search for 380k legal documents using embeddings. Evaluate against BM25 and design a hybrid system. Get a verifiable certificate.

The scenario

The Frankfurt legal-SaaS (around EUR 14M ARR, sells to mid-market law firms, retention bottleneck is search quality) has 2 well-funded US competitors closing in — semantic search is table stakes for renewal.

CredentialBlockchain-anchored
ShareableLinkedIn-ready
LanguageEnglish
PaceSelf-paced

The Brief

What you'll do, and what you'll demonstrate.

Build a semantic-search service that beats the keyword baseline on recall-at-10 and ships as a hybrid architecture.

Earning criteria — what you'll demonstrate

  • Use pre-trained sentence-transformers for cross-lingual semantic search
  • Compare semantic vs keyword retrieval with proper IR metrics
  • Design hybrid retrieval architectures with reranking
  • Translate evaluation results into production architecture decisions

Program Fit

Where this fits in your program.

Sharpens the same skills your degree expects you to demonstrate.

Machine Learning (CS Elective)

Master · General Studies

Strong alignment

This challenge maps to Machine Learning (CS Elective) at the Master level. It sharpens the same practical skills your coursework expects — but in a real industry context with actual constraints and deliverables.

One more thing

You can put a credential on your CV by Friday.