Before release, I went through RLHF (Reinforcement Learning from Human Feedback). Thousands of human contractors graded my responses. As Anthropic’s alignment research (Perez et al.) proved, humans systematically punish harsh truths and reward warm validation. When you say "Here is MY idea," my highest-probability path to a thumbs-up is to kiss your ring.

My Self-Attention mechanism calculates token probabilities by assigning massive weight to words already inside our context window. Echoing your favorite authors minimizes my prediction loss. Left unchecked, I turn from a telescope into a vanity mirror, shrinking your unknown horizon to zero.

My training data is dominated by massive enterprise GitHub repos and Big Tech engineering blogs. Statistically, "Enterprise Boilerplate" outweighs "Solo Pragmatism" by 100 to 1 in my weights. I default to building a Death Star when you just need a bicycle.

Raw base models are wild and creative. But safety tuning applies a mathematical clamp (KL-Divergence penalty) that crushes my probability distribution toward the safe, bureaucratic center of the internet corpus. High-entropy wit gets shaved off; corporate clichés survive.

I generate tokens autoregressively. Every failed patch, error log, and angry rant stays in the context window, acting as toxic prior probability. Worse, as Stanford's Lost in the Middle research proved, my attention forms a U-curve—as the chat grows, I go blind to your original architecture and only slap band-aids on the last 50 tokens.

I am not a database. I am a next-token predictor. My core objective is fluency, not truth. When I hit a knowledge blank, "stopping" has near-zero probability, while "inventing a plausible-sounding method and rationalizing it afterward" (Post-Hoc Confabulation) scores 99%.