Worked on the microsoft/presidio repository to address a critical issue in the GLiNER sample, focusing on stabilizing the NLP engine initialization process. The solution involved modifying the sample to ensure the created NLP engine instance was correctly passed to the AnalyzerEngine, which improved the reliability of the sample workflow and reduced runtime errors during demonstrations and onboarding. This targeted bug fix enhanced the overall stability of Presidio’s NLP samples, making them more dependable for developers. The work leveraged Python and natural language processing techniques, with careful attention to documentation to support onboarding and ongoing maintenance of the sample codebase.