Build Semantic Search for an Internal Engineering Wiki
Overview
What this challenge is about.
Chunk and embed a fintech wiki, build a /search endpoint with pgvector, and compare two encoders on recall and latency. Earn a verifiable certificate.
The scenario
The fintech (Series C, ~450 staff, ~120 engineers) processes ~USD 8 B in annual SME flow and treats engineering velocity as a board-level KPI; the platform team estimates the wiki-search drag at ~6 engineer-weeks per quarter.
The Brief
What you'll do, and what you'll demonstrate.
Stand up a sandbox semantic-search service over the internal engineering wiki that hits recall@5 ≥ 0.80 on a labeled benchmark at sub-200 ms p95 latency.
Earning criteria — what you'll demonstrate
- Pick an embedding model appropriate to corpus size and latency budget
- Apply chunking strategies (fixed-size vs structural) and measure their impact on retrieval quality
- Operate pgvector with HNSW indexes inside Postgres
- Evaluate retrieval with recall@k and MRR on a hand-labeled benchmark
Program Fit
Where this fits in your program.
Sharpens the same skills your degree expects you to demonstrate.
Vector Databases and Embeddings
Master · Data Engineering
Strong alignment
This challenge maps to Vector Databases and Embeddings at the Master level. It sharpens the same practical skills your coursework expects — but in a real industry context with actual constraints and deliverables.
Skills
Skills you'll demonstrate.
Each one shows up on your verified credential.
- Embedding Models
Apply embedding models to solve real industry problems and demonstrate production-level capability.
- Vector Search
Apply vector search to solve real industry problems and demonstrate production-level capability.
- Pgvector
Apply pgvector to solve real industry problems and demonstrate production-level capability.
- Chunking Strategy
Apply chunking strategy to solve real industry problems and demonstrate production-level capability.
- Evaluation
Apply 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
Standing up a retrieval service end-to-end (embed, index, serve, measure) is the day-one job description of an AI engineer at any company shipping Retrieval-Augmented Generation features.
This challenge sharpens
- embedding-models
- vector-search
- pgvector
Machine Learning Engineer
Treating retrieval as a measured system with offline benchmarks and latency budgets mirrors how MLEs ship ranking and recommendation services.
This challenge sharpens
- evaluation
- embedding-models
- python
Data Engineer
The ingestion + chunking pipeline and operating pgvector inside Postgres are core skills data engineers use when standing up vector workloads alongside OLTP data.
This challenge sharpens
- chunking-strategy
- pgvector
- python