Post by Gautam Girotra

𝐖𝐡𝐲 "𝐋𝐢𝐧𝐞𝐚𝐫 𝐑𝐀𝐆" 𝐟𝐚𝐢𝐥𝐬 𝐚𝐭 𝐭𝐡𝐞 𝐄𝐧𝐭𝐞𝐫𝐩𝐫𝐢𝐬𝐞 𝐥𝐞𝐯𝐞𝐥. Standard RAG is a search engine with a voice box. For dense technical/legal contracts, it’s not enough. The failure mode isn't the retrieval; it’s the reasoning gap. I’ve been moving toward 𝐀𝐠𝐞𝐧𝐭𝐢𝐜 𝐑𝐀𝐆 architectures using LangGraph to solve this. We’ve shifted from linear chains to 𝐬𝐭𝐚𝐭𝐞𝐟𝐮𝐥 𝐥𝐨𝐨𝐩𝐬. 𝐖𝐡𝐚𝐭 𝐚𝐜𝐭𝐮𝐚𝐥𝐥𝐲 𝐦𝐚𝐭𝐭𝐞𝐫𝐬 𝐢𝐧 𝐩𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧 𝐫𝐢𝐠𝐡𝐭 𝐧𝐨𝐰: 𝐀𝐝𝐚𝐩𝐭𝐢𝐯𝐞 𝐑𝐞𝐭𝐫𝐢𝐞𝐯𝐚𝐥: Don't just "fetch K documents." Use a router to determine if the query even needs external data or if it’s a reasoning-only task. 𝐃𝐲𝐧𝐚𝐦𝐢𝐜 𝐒𝐭𝐚𝐭𝐞 𝐑𝐞𝐜𝐨𝐯𝐞𝐫𝐲: If a critique node fails, the system shouldn't crash; it should backtrack. Persistence isn't just about memory; it's about error correction. 𝐂𝐨𝐧𝐭𝐞𝐱𝐭 𝐏𝐫𝐮𝐧𝐢𝐧𝐠: In the age of massive token windows, the skill is no longer "finding the needle", it's removing the haystack, so the LLM doesn't get distracted by "noise." 𝐓𝐡𝐞 𝐑𝐞𝐚𝐥𝐢𝐭𝐲: In 2026, the LLM is the engine, but the Orchestration is the driver. Don't build a faster engine; build a smarter driver. #LangGraph #AgenticAI #GenerativeAI #RAG #MLOps #DataScience