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Large Language Models (LLMs) can give notoriously inconsistent responses when asked the same question multiple times. For example, if you ask for help writing an Elasticsearch query, sometimes the generated query may be wrapped by an API call, even though we didn’t ask for it. This sometimes subtle, other times dramatic variability adds complexity when integrating generative AI into analyst workflows that expect specifically-formatted responses, like queries.

Since we launched Finance and Supply Chain Workflows in May, I've had a lot of conversations with ServiceNow customers. They want to know how we're applying the Now Platform to address the challenges their business stakeholders have been facing across the source-to-pay process.

While there are an increasing number of off-the-shelf machine learning (ML) solutions that promise to adapt to your specific requirements, organizations that are serious about investing in ML for the long term are building their own workflows tailored exactly to their data and the outcomes they expect. To make full use of this investment, ML models must be kept up to date and working from the freshest available data.

Transnetyx is an automated genotyping company dedicated to providing biomedical researchers with faster, easier, and more accurate results worldwide.