The latest articles on DEV Community by Marium Tariq (@marium_tariqct254_610e3d). https://dev.to/marium_tariqct254_610e3d https://media2.dev.to/dynamic/image/width=90,height=90,fit=cover,gravity=auto,format=auto/https:%2F%2Fdev-to-uploads.s3.us-east-2.amazonaws.com%2Fuploads%2Fuser%2Fprofile_image%2F3996759%2F67fd7a64-4724-4baf-b0ec-d631a9fd949e.jpg https://dev.to/marium_tariqct254_610e3d en Marium Tariq Mon, 22 Jun 2026 11:01:31 +0000 https://dev.to/marium_tariqct254_610e3d/how-to-evolve-a-linear-langchain-rag-pipeline-into-a-stateful-multi-agent-consensus-architecture-3pcb https://dev.to/marium_tariqct254_610e3d/how-to-evolve-a-linear-langchain-rag-pipeline-into-a-stateful-multi-agent-consensus-architecture-3pcb <p>We’ve all built the classic, straight-line RAG pipeline: chunk a document, toss it into a vector database like FAISS, and feed the context into an LLM. In controlled settings, it feels like magic. But the moment you move into production, reality hits hard: document layouts vary wildly, hallucinations slide through undetected, and conflicting sources break your pipeline entirely. This is exactly the wall I hit with my baseline GroqRAG project. Here is a structural breakdown of why linear pipelines fail, and the architectural blueprint I designed to solve it using LlamaIndex retrieval filtering, LangGraph stateful routing, and a multi-agent consensus 'Judge' workflow.<br> 👉 <strong>Read the full Whitepaper/track the implementation on GitHub:</strong> <a href="https://github.com/tariqmarium6-ux/GroqRAG_Project" rel="noopener noreferrer">https://github.com/tariqmarium6-ux/GroqRAG_Project</a><br> link to whitepaper:<a href="https://drive.google.com/file/d/1LpEi675vVlI1M5-Jm8xK3WPEgPsiOioX/view" rel="noopener noreferrer">https://drive.google.com/file/d/1LpEi675vVlI1M5-Jm8xK3WPEgPsiOioX/view</a></p> ai rag architecture