Decoding Drift: Trustworthy Prompt Optimization in High-Stakes AI Environments
Bibliographic record
Abstract
The reliability and trustworthiness of Large Language Models (LLMs) take shape as more high-stake industries like healthcare, finance, and legal systems embrace them as their applications. Nonetheless, the issue of prompt drift (minor cumulative deviations in the model behaviour because of differences in the prompt structure and contextual framing) is a major threat to model outputs that are consistent. We have introduced a sound solution to the trustworthy prompt optimization (TPO) problem in the form of a systematic and comprehensive methodology that mitigates the drift issue in three major ways: (1) a drift-sensitive evaluation criterion measuring semantic and policy deviation in the responses of LLCs through LLPitals, (2) a prompt tuning algorithm that balances performance and interpretability based on reinforcement learning, and (3) a module that allows the calibration of a human in the loop in high-stakes decision situations. The outcomes of experiments in three benchmark datasets of clinically relevant, fraud detection and legalistic reasoning tasks show that the proposed TPO framework is up to 27 percent more stable in output and 19 percent more faithful to the facts compared to the competing prompt engineering baselines. This study preconditions ethically sound and reproducible prompt design in safety-sensitive AI products, providing a template of compliance of regulations and ethical assurance within the framework of LLM implementation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.110 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".