Deep dives into tech, design, and AI exploration
In actual deliveries from our AI Lab, many engineers fall into a common illusion when dealing with RAG (Retrieval-Augmented Generation): “As long as the model’s
In the actual delivery process of AI Labs, the most common pitfall many teams fall into is: equating the iterative tuning of Prompts with the engineering delive
In the delivery process of an AI Lab, the most dangerous illusion is the success of “End-to-End” workflows.
In the actual delivery process of an AI Lab, the most dangerous moment is often not when the model performs poorly, but when it appears to perform “perfectly.”
In many AI Lab delivery scenarios, I frequently observe a widespread misconception: teams habitually equate the delivery logic of AI projects with that of tradi
In many AI lab delivery scenarios, I frequently observe a pervasive anxiety: developers spend days meticulously tweaking a single word or punctuation mark withi
In many AI Lab delivery scenarios, I often see a highly tempting trap: the pursuit of “end-to-end” full automation.
In many AI Lab delivery scenarios, I frequently observe an extremely dangerous pattern: developers hardcode complex business logic, data cleaning rules, and eve
In many AI Lab delivery scenarios, the most anxiety-inducing moment isn’t when the model lacks intelligence, but when it “occasionally” makes mistakes. When cli