Rewards · $700 cash pool · 5 tracks + content bounty

Build it well.
Walk away with cash.

Win from a $700 prize pool across five tracks, plus TigerGraph recognition and certificates for all participants.

🏆 Winner · 1st Place
$250
≈ ₹23,499 INR

Best GraphRAG Inference System

Awarded to the team that builds the most compelling, production-ready three-pipeline benchmark — biggest verified token reduction, accuracy held or improved, and the clearest story told with their numbers.

Cash prizeWinner certificateTigerGraph recognitionFeature on TigerGraph channels
1st Runner-Up · 2nd Place
$150
≈ ₹14,099 INR

Outstanding Implementation

Strong execution across all three pipelines with clear, credible benchmarks and accuracy that holds vs Basic RAG.

Cash prizeRunner-up certificate
2nd Runner-Up · 3rd Place
$100
≈ ₹9,399 INR

Strong Graph Utilization

Strong graph utilization and efficient inference design — best use of multi-hop reasoning to keep tokens honest while accuracy holds.

Cash prize3rd place certificate
TigerGraph Community Leads' Exclusive
$100
≈ ₹9,399 INR

Creativity, Innovation & Community Spirit

A special award picked by TigerGraph's community leads — for the team that surprises us with the most creative angle, or who shows up for the community along the way.

Cash prizeCommunity certificate
Content Creation Bounty · Separate sign-up
$100
≈ ₹9,399 INR

Content Bounty

Earn extra by sharing your hackathon journey. Create blog posts, social posts, video walkthroughs, or tutorials about your GraphRAG build and stand a chance to win a $100 bounty. Tag #GraphRAGInferenceHackathon and @TigerGraph. Sign up separately on Luma.

Sign up on Luma →Reshared by TigerGraph

Appreciation Certificates

All Top 10 teams receive appreciation certificates from TigerGraph.

Participation Certificates

Every participant who submits a valid solution receives a digital participation certificate.

What judges look for

Judging criteria

Four weighted criteria, totalling 100%. Token reduction only counts if accuracy holds — a 70% token cut with 20% accuracy loss is a regression, not a win.

CriteriaWeightWhat we're looking for
Token Reduction30%% improvement in tokens and cost per query vs Basic RAG. Show us the numbers.
Answer Accuracy30%Quality maintained or improved vs Basic RAG. Evaluated with LLM-as-a-Judge + BERTScore.
Performance20%Latency, throughput, and overall system efficiency. A fast pipeline matters as much as a cheap one.
Engineering & Storytelling20%Clean architecture, working dashboard, clear demo video, blog post that tells the story.
Bonus points · Accuracy

Strong accuracy is heavily rewarded. Submissions hitting these bars earn extra points: LLM-as-a-Judge pass rate ≥ 90% and BERTScore F1 rescaled ≥ 0.55 (or raw ≥ 0.88). Hitting both unlocks the maximum bonus.

Required deliverables

All submissions go through Unstop
  • Architecture diagram— clean visual of your system
  • Comparison dashboard— side-by-side metrics for all 3 pipelines
  • Benchmark report— tokens, cost, latency, accuracy per pipeline
  • Demo video— 5–7 minute walkthrough
  • Public GitHub repo— built on the TigerGraph GraphRAG repo
  • Blog post— Medium, Hashnode, Dev.to, or your own
  • Social media post— LinkedIn / X · tag @TigerGraph #GraphRAGInferenceHackathon
  • Product feedback interview— short call with TigerGraph (Top 5–10 only)
Reference implementation

github.com/tigergraph/graphrag

TigerGraph's official GraphRAG reference. Use it as your starting point for graph construction, entity + community identification, and multi-hop reasoning patterns. Clone, extend, benchmark.

Open repo →