The latest articles on DEV Community by chinmayi-r-hegde (@chinmayirhegde). https://dev.to/chinmayirhegde https://media2.dev.to/dynamic/image/width=90,height=90,fit=cover,gravity=auto,format=auto/https:%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Fuser%2Fprofile_image%2F3919291%2F98554d10-980a-48c1-9781-6a6061b109df.png https://dev.to/chinmayirhegde en chinmayi-r-hegde Fri, 08 May 2026 06:29:43 +0000 https://dev.to/chinmayirhegde/how-i-built-a-graphrag-system-that-saves-70-85-llm-tokens-using-tigergraph-4i63 https://dev.to/chinmayirhegde/how-i-built-a-graphrag-system-that-saves-70-85-llm-tokens-using-tigergraph-4i63 <h2> What I Built </h2> <p>MediGraph is a medical question answering system that compares <br> three AI pipelines — LLM Only, Basic RAG, and GraphRAG — and <br> proves that graphs dramatically reduce token consumption.</p> <h2> The Problem </h2> <p>LLMs are expensive. Every query consumes tokens. Companies pay <br> more every quarter just to run AI in production.</p> <h2> My Solution — GraphRAG with TigerGraph </h2> <p>Instead of dumping entire documents into the LLM prompt, <br> GraphRAG traverses a knowledge graph and returns only the <br> exact facts needed.</p> <h2> Results </h2> <p>| Pipeline | Tokens | Cost |<br> | LLM Only | 813 | $8.1e-05 |<br> | Basic RAG | 173 | $1.8e-05 |<br> | GraphRAG | 125 | $1.5e-05 |</p> <p>GraphRAG saves 70-85% tokens vs LLM Only!</p> <h2> Tech Stack </h2> <ul> <li>TigerGraph Savanna — graph database</li> <li>Groq API — LLM inference</li> <li>FAISS — vector search</li> <li>Streamlit — dashboard</li> <li>Python 3.11</li> </ul> <h2> How It Works </h2> <ol> <li>User selects a disease</li> <li>TigerGraph traverses Disease → Symptoms → Drugs</li> <li>Compact structured context is passed to LLM</li> <li>LLM generates precise answer</li> </ol> <h2> GitHub </h2> <p><a href="https://github.com/chinmayi-r-hegde/graphrag-hackathon" rel="noopener noreferrer">https://github.com/chinmayi-r-hegde/graphrag-hackathon</a></p> rag ai beginners python