This paper investigates how large language models generate precise responses despite operating within vast networks of conceptual associations encoded in language. It proposes that prompts function as activation signals within a high-dimensional web of knowledge derived from statistical patterns in language, generating a space of potential semantic trajectories that are progressively constrained through attention mechanisms and probabilistic filtering — a process termed conceptual convergence. The paper introduces conceptual gravity as a subsidiary mechanism: differential density across the conceptual web causes semantic trajectories to be drawn toward high-density regions, explaining both reliable coherence and the structural predictability of hallucination. The account is situated in relation to adjacent positions (Bender et al., 2021; Shanahan, 2024; Harnad, 1990) and grounded in classical epistemological frameworks — Quine’s web of belief, Frege’s sense and reference, Peirce’s convergent inquiry, and Collins and Loftus’s spreading activation theory. The paper further develops multi-turn interaction dynamics through the concept of convergence lock-in, addresses the cultural specificity of the conceptual web, and identifies empirically testable predictions arising from the conceptual gravity proposal. The result is a philosophically grounded framework that characterizes prompting as guided conceptual navigation and illuminates the hybrid epistemic activity that emerges from sustained human–AI interaction.