When using AI for semantic search, how can we ensure the relevance of the results?

Ensuring semantic search relevance requires implementing robust contextual understanding mechanisms, typically through advanced embedding models and ranking algorithms. This is achievable by optimizing both data representation and retrieval methodology. Fundamental approaches include employing dense vector representations from transformer-based models to capture semantic meaning, applying relevance ranking techniques like cross-encoders for precise scoring, and designing nuanced query analysis. Careful fine-tuning of embedding models on domain-specific data enhances contextual matching, while hybrid methods combining lexical and semantic matching can improve precision. Continuous evaluation using metrics such as MRR or NDCG with human-annotated test sets is critical. Implementing relevance starts with selecting and fine-tuning appropriate embedding architectures like BERT variants for query-document encoding. Develop a multi-stage retrieval pipeline: initial candidate generation using approximate nearest neighbor search, followed by neural re-ranking. Rigorously evaluate performance across diverse query types using established benchmarks. Iteratively refine models based on user feedback logs and A/B testing to maintain relevance across evolving data distributions, thereby reducing irrelevant results and enhancing user satisfaction.