Summary

The article discusses LinkedIn's transformation of its search technology stack, focusing on the integration of large language models (LLMs) to enhance search experiences. It details the challenges and innovations involved in deploying LLMs at scale, including query understanding, semantic retrieval, and ranking processes. The use of AI-driven job and people search features aims to provide more relevant and personalized results. Additionally, the article highlights the importance of continuous relevance measurement and quality evaluation in maintaining a high-quality search experience.

💭 Your Thoughts

This is a very rich tech blog, and I learned a lot. - a query embedding, ranking pipeline, feature generation high level architecture - “Measuring relevance quality is essential to delivering a great search experience on LinkedIn.” totally agreed - generate labeled data for training - my every click is a valuable data input for them, i should charge them - how to continuously measuring quality of search system - Small language model ranking, is interesting, i thought about this in my cross domain recommendation engine before, i will definitely try it in the future with their guidance.

Data Problems Addressed

Technologies Referenced