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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.
Natural Language Processing
Natural Language Processing (NLP) is a branch of artificial intelligence that focuses on the interaction between computers and humans through natural language. While developers communicate with computers through code, NLP allows us to interact with technology using human language, making technology more accessible and user-friendly.
This white paper describes NLP and how it allows us to communicate with computers as well as AI and machine learning models through natural human language.
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.
Building confidence in LLM outputs
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.
More LLM resources
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The 5 steps in our ML model training process
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Speed up CIM review with LLMs
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Minimize hallucination risk while maximizing LLM output
Guide
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How finance teams can leverage LLMs
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Revolutionize your data management: how ML transforms data operations
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Model choice: An open architecture strategy for using LLMs
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The art of prompting: how to win [conversations] and influence [LLMs]
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A high-performance approach to personalizing an LLM
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