Blend Hybrid Search

In the world of AI-powered search, relevance is everything. Go beyond the limits of pure keyword or vector search in Blend Hybrid Search, an intermediate course for developers and ML engineers. You will learn to build a state-of-the-art search system by combining the precision of keyword matching (like BM25) with the semantic power of dense vectors. This course provides a complete, hands-on framework for optimizing search performance using open-source tools as our implementation example. You won't just build a hybrid search function; you will master the art of tuning it. Through a project-driven approach, you will learn to systematically adjust weighting parameters and use the industry-standard NDCG metric to objectively measure and prove the impact of your changes. You will leave with a reusable evaluation script and a data-driven methodology for squeezing the maximum relevance from any search application

• Implement a hybrid search system tuning keyword and vector scores with NDCG metric
• Build advanced search combining keyword matching precision and semantic vectors
• Systematically adjust weighting parameters and measure impact with NDCG
• Apply data-driven evaluation methodology using open-source tools

• Basic familiarity with topic terminology
• Readiness for applied exercises and case studies