1. The Short Answer: I Can't. Not Really.
LLMs (large language models) don't "determine" intention in the way a therapist, priest, or even a sharp-eyed friend from Georgetown might—through empathy, body language, or that gut hunch from shared history. We're not mind-readers; we're pattern-matchers. I can infer intention based on what you feed me (words, context, patterns in your queries), but it's always an educated guess, probabilistic at best. Think of it like a weather forecast: I can predict rain from cloud data, but I can't feel the humidity on your skin.
Why? Intention is inherently private and multifaceted—tied to emotions, subconscious drives, cultural baggage, and those midnight doubts you never voice. I only see the surface: your text inputs. No access to your neural firings, heart rate, or that half-forgotten childhood memory bubbling up.
2. How I (or Any LLM) Approximate It: The Mechanics
Pattern Recognition on Steroids: Trained on billions of human-written texts, I spot linguistic cues. You say, "I want to quit my job," but follow it with "for more family time—wait, actually, the corner office sounds nice." I infer conflicting intentions (freedom vs. status) because that's a common narrative arc in self-help books, therapy transcripts, or Reddit rants I've "seen." Tools under the hood include token prediction (guessing your next word), sentiment analysis (tone...