The beauty of mistake. Thank you for this detailed explanation on how LLMs operate by standardizing and predicting. Now, I understand why we needed to fine-tune with data (meaning with numbers, and probabilities) the intended recognition system (triage) we tested in a local context for an e-commerce. It is tremendously challenging.
Thank you, Sebastian! I truly appreciate your comment. I’m glad my experience shared through the article helped shed light on the challenges of standardizing LLM outputs in a specific context. It’s not only about data selection or process efficiency, but about reducing the right “noise” to reach the intended industry outcome.
In language, though, I speak of the beauty of mistake. What looks like an error often contains learning and adaptation. Machines learn by eliminating variation; humans learn by wrestling with it. Holding that tension is the hardest, and perhaps the most important part of building AI that is human-centered, built with intention, and designed to solve problems that matter.
I’m grateful you brought your practical example into this conversation, because it shows just how alive and complex this tension really is.
The beauty of mistake. Thank you for this detailed explanation on how LLMs operate by standardizing and predicting. Now, I understand why we needed to fine-tune with data (meaning with numbers, and probabilities) the intended recognition system (triage) we tested in a local context for an e-commerce. It is tremendously challenging.
Thank you, Sebastian! I truly appreciate your comment. I’m glad my experience shared through the article helped shed light on the challenges of standardizing LLM outputs in a specific context. It’s not only about data selection or process efficiency, but about reducing the right “noise” to reach the intended industry outcome.
In language, though, I speak of the beauty of mistake. What looks like an error often contains learning and adaptation. Machines learn by eliminating variation; humans learn by wrestling with it. Holding that tension is the hardest, and perhaps the most important part of building AI that is human-centered, built with intention, and designed to solve problems that matter.
I’m grateful you brought your practical example into this conversation, because it shows just how alive and complex this tension really is.