Overview
What this challenge is about.
Wire a Knowledge Graph into a Pharma RAG Assistant. Advanced challenge in code. Writing production code that solves real engineering problems, earn a blockch...
The Brief
What you'll do, and what you'll demonstrate.
Cut RAG hallucination rate on drug-target-disease relationships by grounding answers in a curated knowledge graph.
This is not a coding exercise. It is the work a software engineer does between a Jira ticket and a merged PR. That distinction matters to every hiring manager who has seen candidates solve LeetCode problems and none who have shipped production code under real constraints.
When you finish, you will have something most graduates do not: a real-world deliverable, verified by Ewance, that you can show to a hiring manager and say "I did this. Here is the proof."
Earning criteria — what you'll demonstrate
- Wire a knowledge graph into a RAG retrieval pipeline
- Extract entities from natural-language questions for KG lookup
- Evaluate hallucinations against a structured ground-truth source
- Reason about the limits of KG grounding when the KG is incomplete
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.
- Kg Grounded Rag
Apply kg grounded rag to solve real industry problems and demonstrate production-level capability.
- Sparql
Apply sparql to solve real industry problems and demonstrate production-level capability.
- Entity Linking
Apply entity linking to solve real industry problems and demonstrate production-level capability.
- Hallucination Evaluation
Apply hallucination evaluation to solve real industry problems and demonstrate production-level capability.
- Knowledge Graphs
Apply knowledge graphs to solve real industry problems and demonstrate production-level capability.
- Retrieval Augmented Generation
Apply retrieval augmented generation 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
Wiring a KG into a RAG pipeline with measurable hallucination reduction is exactly the work AI engineers do at any high-stakes RAG product.
This challenge sharpens
- kg-grounded-rag
- retrieval-augmented-generation
- entity-linking
AI Solutions Architect
Designing the entity-linking + KG-query layer plus the limits memo is the AI solutions architect's output in regulated-industry RAG.
This challenge sharpens
- kg-grounded-rag
- knowledge-graphs
- retrieval-augmented-generation
AI Safety Researcher
Measuring hallucinations against a structured source is a methodological contribution that safety researchers ship in regulated AI deployments.
This challenge sharpens
- hallucination-evaluation
- kg-grounded-rag
- knowledge-graphs