Ekrem Sarı
Professional Experience
During his tenure as an Assessor at Yandex, he evaluated search results using proprietary frameworks and automated protocols. He implemented QA testing through data annotation, relevance scoring, and user intent mapping across 10,000+ queries monthly, while conducting technical assessments including performance monitoring and spam detection using ML feedback loops.Research Interest
At AIMultiple, his research is centered on the performance and benchmarking of end-to-end AI systems.He contributes to a wide range of projects, including Retrieval-Augmented Generation (RAG) optimization, extensive Large Language Model (LLM) benchmarking, and the design of agentic AI frameworks.Ekrem specializes in developing data-driven methodologies to measure and improve AI technology performance across critical metrics like accuracy, efficiency, cost, and scalability.His analysis covers the entire technology stack, from foundational components like embedding models and vector databases to the infrastructure required for deploying AI agents, such as remote browser solutions and web automation platforms.
Education
Ekrem holds a bachelor's degree from Hacettepe Üniversitesi and a master's degree from Başkent Üniversitesi.Latest Articles from Ekrem
Top Serverless Functions: Vercel vs Azure vs AWS
Serverless functions enable developers to run code without having to manage a server. This allows them to focus on writing and deploying applications while infrastructure scaling and maintenance are handled automatically in the background. In this benchmark, we evaluated 7 popular cloud service providers following our methodology to test their serverless function performance.
Top 20+ Agentic RAG Frameworks
Agentic RAG enhances traditional RAG by boosting LLM performance and enabling greater specialization. We conducted a benchmark to assess its performance on routing between multiple databases and generating queries. Explore agentic RAG frameworks and libraries, key differences from standard RAG, benefits, and challenges to unlock their full potential.
Best RAG tools: Frameworks and Libraries
RAG (Retrieval-Augmented Generation) improves LLM responses by adding external data sources. We benchmarked different embedding models with various chunk sizes to see what works best. Explore the RAG frameworks and tools, what RAG is, how it works, its benefits, and the current situation in the LLM landscape.
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