An agent's answer is never better than the knowledge behind it. Posts on building a knowledge base, grounding with RAG, data quality and freshness, institutional memory, data ownership and context management.
For decades, our relationship with health data has been reactive. We wait for a yearly check-up, look at a static blood report, or glance at a step counter to see if we hit a number. The data is there, but it's fragmented. It tells us what happened, but rarely why it matters or how to change the trajectory of our biological clock.
For years, the conversation around Generative AI has been dominated by "The Great Training War." We’ve heard the arguments about fair use, copyright infringement, and the philosophical debate over whether a machine "learning" from a book is any different from a human student reading one in a library. But as the dust settles on massive legal settlements and courtroom battles, a new, more mundane, yet far more dangerous problem has emerged: the crisis of record-keeping.
Imagine hiring a brilliant executive assistant. On Monday, you spend two hours explaining your company's brand voice, your preference for concise reports, and the specific nuances of your upcoming product launch. You have a great brainstorming session. Then, on Tuesday, when you ask them to actually draft the announcement emails, they look at you blankly and ask, "Could you remind me again what the product is and who we are targeting?"
For years, the narrative around Large Language Models (LLMs) was one of infinite abundance. We were told that the internet—the vast, chaotic library of Reddit threads, Wikipedia entries, and digitized news archives—was a feast large enough to sustain AI growth for decades.
For decades, the Great Library of Alexandria has served as the ultimate historical metaphor for the tragedy of lost knowledge. We mourn the fire that erased centuries of human thought in a single catastrophe. But today, we are witnessing a different kind of erasure—one that doesn't happen with a sudden blaze, but with the methodical, industrial hum of high-speed scanners and the clinical snap of book spines being severed.
For years, the corporate narrative surrounding Generative AI was simple: Adopt it as fast as possible or get left behind. Companies rushed to integrate LLMs into their workflows, treating these tools as magic wands for productivity. But the tide is turning.
The recent decision by the U.S. administration to force Anthropic to take its newest models, Fable 5 and Mythos 5, offline has sent shockwaves through the tech world. While the official reason cited "national security concerns" and export controls to prevent foreign nationals from accessing
The recent reports surrounding Anthropic’s "Mythos" model have sent a ripple of anxiety through the tech world. When rumors surface that a powerful AI model may have been accessed by foreign entities—potentially leading to "distillation" (where a smaller model is trained to mimic a larger, more
The landscape of artificial intelligence is shifting. For years, the primary goal of AI in business was automation—handling repetitive tasks or answering common questions. However, we are now entering the era of predictive intelligence.
