A beginner-friendly online coding challenge to prove that graphs make LLM inference faster, cheaper, and smarter. Build three pipelines side-by-side — LLM-Only, Basic RAG, GraphRAG — and let the numbers tell the story.
Your mission is an interactive comparison dashboard: enter one query, run it through all three pipelines at once — LLM-Only, Basic RAG, and GraphRAG — and display side-by-side answers with tokens, latency, cost, and accuracy.
A prompt goes in, an output comes out. No retrieval — just the model reasoning from scratch. Your worst-case baseline, and the upper bound on cost.
The industry standard today. Vector embeddings find similar chunks and stuff them into the prompt. Helpful, but it can't reason across relationships between entities.
Built on the TigerGraph GraphRAG repo. TigerGraph handles entity extraction, relationships, and multi-hop reasoning. The LLM synthesizes from a clean, focused subgraph.
Four separate layers — scalable, reusable, and ready for real production. A clean reference architecture for the Engineering & Storytelling axis (20% of judging).
TigerGraph handles entity extraction, relationships, and graph queries.
Decides when to use graph, when to call the LLM, and how they cooperate.
Generates the final answer using filtered, structured context from the graph.
Runs the benchmarks and populates your comparison dashboard.
Registrations close May 10, 2026 · 11:59 PM IST. Don't be the team still reading docs on day one.
Exploring GenAI for the first time. We'll provide docs, starter code, and mentoring for the Top 10.
Curious about graph databases. If you know Python and have poked at LLMs or APIs, you're ready to build.
Building production RAG. Push past vector-only retrieval — ship a graph-aware inference stack with real benchmarks.