GraphAugmented Intelligence

🏆 1st Place – SCE Hacks 2025
Designed and implemented Graph-Augmented Intelligence (GAI), a prompt-aware, knowledge graph–driven Retrieval-Augmented Generation (RAG) framework that improves factual grounding and significantly reduces hallucinations in Large Language Models (LLMs). The system combines structured knowledge graphs with LLM reasoning to enable accurate, low-latency responses for complex, multi-hop queries.

GAI dynamically extracts minimal, prompt-aware contextual subgraphs from a knowledge graph and injects them into the LLM generation pipeline, enabling grounded reasoning across multi-domain information. The framework was validated on large-scale biomedical knowledge graphs, supporting reasoning over entities such as diseases, drugs, genes, and biological relationships.

Key Features:

• 🧠Hallucination Reduction: 40% reduction in hallucinated responses.
• 🔗Multi-hop Reasoning: Enhanced accuracy in complex queries achieving 35% improvement.
• 🛒 Response Latency: < 2s average response time.
• 📱 Knowledge Recall: 92% accuracy in retrieving relevant information.

Architecture Overview:

• Knowledge Graph Construction: Integrated multi-domain data into a unified knowledge graph using Neo4j.
• Semantic Embeddings: Employed graph neural networks to generate context-aware embeddings for nodes and relationships.
• Prompt-Aware Retrieval: Developed a retrieval mechanism that leverages prompt context to fetch relevant graph segments.
• LLM Integration: Combined retrieved graph data with LLMs (e.g., GPT-4) for response generation.
• Evaluation Framework: Established metrics for assessing hallucination rates, response accuracy, and latency.

Technical Highlights:

• Implemented a robust knowledge graph architecture using Neo4j and graph neural networks.
• Multi-model query support for diverse data sources.
• Explainable AI with reasoning capabilities.
• Automated graph maintenance and updates Knoweledge graph.