As use of generative AI increases, it's critical to be able to understand how models arrive at the output they do in order to foster trust.
Integrating continuous evaluation of large language models (LLMs) into your CI/CD pipelines keeps undesired changes from impacting your generative AI solutions.
Learn what retrieval augmented generation (RAG) is, how it enhances generative AI like large language models (LLMs), and key considerations for RAG systems.
Large language models (LLMs) are effective tools for testing how well retrieval augmented generation (RAG) systems can enhance a generative AI model.
See how WillowTree partners with the University of Virginia School of Medicine to explore generative AI and use case prioritization in medical education.
With so many diverse large language models to choose from, selecting one for your business can be overwhelming. Here are some key factors to consider.
Part 2 in our series explores how WillowTree benchmarks the accuracy of applications with large language models in the loop, examining the metric of "truthfulness" on a nonbinary scale.
Our latest findings on the differences in behavior between the OpenAI and Azure OpenAI API
WillowTree’s approach to benchmarking accuracy of applications with large language models in the loop.
Hallucinations are an obstacle to building user trust in generative AI applications. Learn about the phenomenon, including best practices for prevention.
A survey conducted by TELUS Digital unveiled consumer concerns about misinformation and bias in generative AI. Learn more.
ChatGPT offers strong insights on how to price a product when given quality prompts, but human oversight is still needed to guide complex pricing decisions.
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