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Technical Resources
Data Science Room
Insights into the machine learning and large language models that power Alkymi, from our team of expert data scientists.
Semantic search
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.
Retrieval Augmented Generation
Retrieval Augmented Generation (RAG) is a method for supplementing large language models with relevant contextual information that they can use for reasoning. It allows for a type of fine-tuning of the responses that can be generated by an LLM, without needing to modify the underlying LLM model.
This white paper describes what Retrieval Augmented Generation is and how it can be used to provide a personalized experience when using an LLM with your data.
More LLM resources
Guide
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How finance teams can leverage LLMs
Blog Post
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Model choice: An open architecture strategy for using LLMs
Blog Post
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