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

Author(s): Eivind Kjosbakken

Originally published on Towards AI.

Optimize your AI search with RAG, contextual retrieval and evaluations

Searching with AI has become prevalent since the introduction of LLMs in 2022. Retrieval augmented generation (RAG) systems quickly adapted to utilizing these efficient LLMs for better question answering. AI search is extremely powerful because it provides the user with rapid access to large amounts of information. You, for example, see AI search systems with

This article discusses the importance of effectively building and improving AI search systems, focusing on techniques like retrieval augmented generation (RAG) and contextual retrieval. It addresses the need for rapid access to information and the implications of system scalability, including response time and uptime, while emphasizing the significance of evaluation methods for continuous improvement. The author provides practical insights to enhance AI search capabilities through various strategies and encourages the integration of advanced features for optimal performance.

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Published via Towards AI