Avoiding the dreaded “semantic drift”
AI systems exhibit a peculiar talent for latching onto ambiguous terms and riding them into interpretive chaos. Like a dog with a favorite stick, once an AI fixates on a particular reading of a word, it will fetch that interpretation relentlessly, regardless of shifting context.
Ask an AI to “optimize” a system. Initially, you mean performance optimization. Three exchanges later, you’re discussing cost optimization. By exchange seven, you’re analyzing workflow optimization. The AI, meanwhile, has been faithfully mixing all three meanings, creating recommendations that simultaneously pursue speed, frugality, and efficiency—often in direct contradiction.
The trap deepens with seemingly concrete terms. “Security” might begin as network security, evolve into data security, then morph into operational security. The AI, accumulating each interpretation, eventually recommends firewalls for your backup procedures and access controls for your packet routing—technically security-related, but contextually absurd.
In iterative analysis, the problem metastasizes. Each refinement introduces subtle semantic shifts that accumulate like measurement errors. What begins as a focused discussion about “user experience” transforms into a hydra-headed analysis spanning interface design, customer support, onboarding processes, and retention strategies. The AI treats each new meaning as additive rather than evolutionary, creating conceptual spaghetti that grows more tangled with each exchange.
The solution requires disciplined semantic hygiene. Lock down central terms early and explicitly. When “performance” enters the conversation, immediately define whether you mean computational speed, user satisfaction, or business metrics. When context shifts, explicitly deprecate old meanings before introducing new ones.
In extended AI collaboration, semantic precision isn’t pedantic—it’s survival. Without tight definitional boundaries, you’re not refining analysis; you’re conducting an increasingly elaborate conversation with a well-meaning but hopelessly confused partner who remembers everything and understands nothing consistently.
In my own practice, I start an analysis with a definitional glossary, making sure the different shades of a words meaning are defined and each conceptual flavor of a word is given a precise and unvarying nomenclature. In the glossary, you can ask the AI as part of your protocol to ask you to clarify which meaning of an potentially ambiguous word you mean, and substitute the unambiguous word or phrase you’ve decided should be used, so that the associated meanings remain static and stable.
Load the dictionary when you start your session, and build on it as you stumble upon more potentially squishy words. Be sure to declare that the words and phrases you share are to be considered “canonical” (this word has special magic powers to an AI. As you do more different analyses in related domains, start off by loading your list of canonical definitions (right after you declare your initial goal and intent for the conversation).
Believe me, I don’t think there’s a single thing you can do that will save you more pain.
