Shahbaz Siddeeq

A closed-loop control architecture for a non-deterministic language model that must satisfy a numeric output constraint, that is, a requirement expressible as a number or an interval and checkable by code, is presented and evaluated.

Transitioning prompt engineering from ad hoc practices to a systematic framework could revolutionize how software engineering integrates AI.

Regrading model outputs can shift correctness labels by 9% and double the performance spread, revealing critical flaws in conventional evaluation methods for LLM code generation.

Epic-organized LLM-generated Gherkin scenarios are rated significantly higher in quality than those generated by a naive baseline, despite similar semantic coverage.