AI Masters Fluent BS, But Forgets the Plot: Why Context Drift Turns Your Prompts into Eternal Rewrites

Researchers have identified a critical issue with AI systems, where fluent output often masks underlying instability, leading to "drift" over time. Despite advances in generation quality, AI output can sound confident but lack coherence, causing constant tweaking and small misalignments. This problem arises from unstable context, where the system lacks a fixed anchor to reason against, resulting in averaging and probabilistic output. Experts like Daniel T. Sasser II are working on solutions, such as the SIM-ONE framework, to address stability and governance in AI systems. The issue is not with prompts or models, but rather with system design, highlighting the need for stability to come before generation. As AI generation quality improves, this problem becomes increasingly difficult to detect, emphasizing the importance of testing and validation to ensure coherence and accuracy. By prioritizing stability, developers can create more effective AI systems that produce consistent and meaningful output.