𝗧𝗵𝗲 𝗠𝗶𝗹𝗹𝗶𝗼𝗻-𝗧𝗼𝗸𝗲𝗻 𝗠𝗶𝘀𝘁𝗮𝗸𝗲: 𝗪𝗵𝘆 𝗕𝗶𝗴𝗴𝗲𝗿 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝗪𝗶𝗻𝗱𝗼𝘄𝘀 𝗪𝗼𝗻'𝘁 𝗙𝗶𝘅 𝗬𝗼𝘂𝗿 𝗗𝗮𝘁𝗮.
We are currently witnessing a massive capital rotation into "Long Context" LLMs. The industry promise is seductive: "Just dump all your PDFs and databases into the prompt, and the AI will figure it out."
It won't. In fact, it is making the hallucination problem worse. We are confusing 𝗰𝗼𝗺𝗽𝘂𝘁𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗰𝗮𝗽𝗮𝗰𝗶𝘁𝘆 with 𝘀𝗲𝗺𝗮𝗻𝘁𝗶𝗰 𝘂𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴.
𝗧𝗵𝗲 𝗦𝗰𝗵𝗼𝗹𝗮𝗿 𝗩𝗶𝗲𝘄: In my doctoral research on the 𝗦𝗼𝗰𝗶𝗼-𝗧𝗲𝗰𝗵𝗻𝗶𝗰𝗮𝗹 𝗚𝗮𝗽, I examined how organizational ambiguity translates into technical failure. This is a classic problem of 𝗢𝗻𝘁𝗼𝗹𝗼𝗴𝗶𝗰𝗮𝗹 𝗔𝗹𝗶𝗴𝗻𝗺𝗲𝗻𝘁. When you feed an agent conflicting definitions of "Length of Stay" from Billing (financial) and Nursing (clinical), a larger context window doesn't resolve the conflict; it amplifies the noise. The AI suffers from "Contextual Drift," where its reasoning degrades as the volume of unrefined data increases.
𝗧𝗵𝗲 𝗣𝗿𝗮𝗰𝘁𝗶𝘁𝗶𝗼𝗻𝗲𝗿 𝗩𝗶𝗲𝘄: In my previous experience, I saw this friction firsthand. The C-Suite wanted to point an LLM at our data lake and ask, "How are we performing?" But the reality below the waterline (the 𝗜𝗰𝗲𝗯𝗲𝗿𝗴 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲) was that our tables didn't speak the same language. We didn't solve it by buying a bigger model. We solved it by enforcing a Governed Semantic Layer. We hard-coded the business logic before the AI ever touched the data.
𝗧𝗵𝗲 𝗧𝗮𝗸𝗲𝗮𝘄𝗮𝘆: You cannot prompt-engineer your way out of a broken schema. If you want "Agentic Intelligence," stop obsessing over the size of the window and start obsessing over the quality of the view.
Originally published on LinkedIn
