How to Scale Your AI Search to Handle 10M Queries with 5 Powerful Techniques

AI search systems are prevalent and typically use Retrieval Augmented Generation (RAG) as their core component. To scale these systems effectively, developers must focus on both response time and uptime. Optimizing response time involves using fast LLMs and streaming outputs, while ensuring high uptime requires robust error handling and backup providers. Continuous evaluation through internal benchmarks and analyzing user queries is crucial for maintaining quality and identifying areas for improvement. Advanced techniques like Contextual Retrieval, which combines vector search with keyword methods, can further enhance search performance.