Data Science Room

A crucial aspect of machine learning systems is having confidence in the output of the models. Confidence enables us to trust that model outputs are reliable and to use those outputs in downstream applications and for decision-making. However, it is well known that large language models (LLMs) sometimes hallucinate the answers to questions, which creates uncertainty about their answers and their utility. 

This white paper discusses some of the approaches that can be used to elicit confidence in LLM outputs and discusses some of the ways we build confidence in model outputs at Alkymi.

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Semantic search is a search methodology where the semantic meaning of words is used to retrieve relevant content in document collections or data sets. This differs from keyword-based search, where documents are retrieved by matching keywords. Semantic search allows for effectively retrieving content that shares the same meaning as a user’s query, despite potentially using different words. 

This white paper provides an overview of semantic search, beginning with a description of traditional keyword-based search. It then discusses word embeddings, what they are, how they’re learned, and how they can be used to build powerful search applications with the help of large language models (LLMs). Lastly, it describes how semantic search is used to power Alkymi’s generative AI products.

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