Large Language Models with Semantic Search

Keyword search has been a common method for search for many years. But for content-rich websites like news media sites or online shopping platforms, the keyword search capability can be limiting. Incorporating large language models (LLMs) into your search can significantly enhance the user experience by allowing them to ask questions and find information in a much easier way.

This course teaches the techniques needed to leverage LLMs into search.

Throughout the lessons, you’ll explore key concepts like dense retrieval, which elevates the relevance of retrieved information, leading to improved search results beyond traditional keyword search, and reranking, which injects the intelligence of LLMs into your search system, making it faster and more effective.  

After completing the course, you will:

• Know how to implement basic keyword search, the underpinnings of many search systems before language models became accessible.
• Enhance keyword search with the rerank method, which ranks the best responses by relevance with the query.
• Implement dense retrieval through the use of embeddings, a powerful NLP tool, which uses the actual semantic meaning of the text to carry out search, and vastly improves results.
• Gain hands-on practice by working with large amounts of data and overcome challenges like varying search results and accuracy.
• Implement language model-powered search into your website or project.

Anyone who has basic familiarity with Python and wants to get a deeper understanding of key technical foundations of LLMs, and learn to use semantic search.