Developed a Semantic Search Demonstration Notebook for the elastic/elasticsearch-labs repository, focusing on enhancing semantic search precision in Elasticsearch using the min_score parameter. The work involved creating an end-to-end Jupyter Notebook that guides users through Elastic Cloud deployment, establishing connectivity to Elasticsearch, and implementing min_score tuning workflows. By providing reproducible experiments and clear setup instructions, the notebook enables product and data science teams to efficiently evaluate precision and recall trade-offs. Leveraging Python, Elasticsearch, and data analysis techniques, the solution accelerates onboarding for new users and supports the integration of tunable semantic search, ultimately improving result relevance and user satisfaction.