📄 中文摘要
在开发 AI 项目时，常常会经历一个被称为“随机壁垒”的阶段。最初，使用顶级 AI 模型（如 GPT-4o 或 Claude 3.5）时，开发速度非常快。然而，随着代码库的增长和对话历史的加深，系统会出现一些潜在的破坏性问题。无论开发者的经验水平如何，都会遇到“上下文混乱”、“架构漂移”和“幻觉循环”等症状。这些问题导致模型逐渐忘记之前建立的逻辑，甚至开始创造违反项目核心原则的规则。解决这些问题需要对 AI 架构进行深入分析和改进。
超越提示工程：为什么你的 AI 架构在泄漏令牌（以及如何通过 FMCF 修复它）
📄 English Summary
Beyond Prompt Engineering: Why Your AI Architecture Is Leaking Tokens (And How to Fix It with FMCF)
The development of AI projects often encounters a phase known as the 'stochastic wall.' Initially, using top-tier AI models like GPT-4o or Claude 3.5 yields impressive speed. However, as the codebase expands and conversation history deepens, subtle yet destructive issues begin to emerge. Developers, regardless of their experience level, face symptoms such as 'Context Smog,' 'Architectural Drift,' and the 'Hallucination Loop.' These issues result in the model forgetting previously established logic and, worse, generating rules that contradict the project's core principles. Addressing these challenges requires a thorough analysis and improvement of the AI architecture.