In February 2026, Brunnert developed a Semantic Search Demonstration Notebook for the elastic/elasticsearch-labs repository, focusing on enhancing semantic search precision in Elasticsearch. Using Python and Jupyter Notebook, Brunnert implemented an end-to-end workflow that leverages the min_score parameter to fine-tune search relevance. The notebook provides a reproducible framework for deploying on Elastic Cloud, connecting to Elasticsearch, and experimenting with precision and recall trade-offs. By documenting setup and min_score tuning, Brunnert enabled product and data science teams to validate semantic search approaches efficiently. The work demonstrated depth in data analysis and practical application of semantic search concepts without addressing bug fixes.