PRISM-T isolates silent LLM model drift

Relying on LLMs as evaluation observers is inherently flawed due to silent vendor updates, making cryptographic baselines necessary for reliable metrics.

* Cryptographic hash-sealing provides a static, verifiable control group to distinguish between external brand shifts and internal model changes.

* Addressing silent model drift is crucial for any business relying on LLMs for long-term sentiment or perception analysis.

* While a pinned panel of static artifacts works well as a benchmark, it might struggle to replicate real-time, dynamic user interactions with models.