Abstract
Performance evaluation for a large language model (LLM) can involve post-hoc analysis with secondary models, an approach that may be computationally expensive and could produce low-fidelity results due to sanitized data logs potentially lacking full user context. A technique is described for in-situ, real-time self-evaluation by an LLM. This technique can involve embedding an evaluation tool definition within a model's system prompt to instruct the model to generate a structured, machine-readable self-assessment of its own response. This self-assessment, which could contain metrics such as topic classification and confidence scores, may be output subsequent to the user-facing response through a separate side-channel. This method can offer a computationally efficient and contextually aware alternative for performance analysis, potentially facilitating more granular analytics by consolidating evaluation into the primary inference call and leveraging the full context available at the time of generation.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Lewis, Peter, "In-Situ LLM Performance Evaluation via a Prompt-Defined Side-Channel for Structured Metadata Output", Technical Disclosure Commons, (April 16, 2026)
https://www.tdcommons.org/dpubs_series/9812