AI powers more efficient and intuitive digital healthcare experiences.
A CIA technique called a canary trap helps us detect AI hallucination risk in large language models (LLMs) enhanced with retrieval augmented generation (RAG).
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.
Intent classification used in concert with a large language model (LLM) and retrieval-augmented generation (RAG) system resulted in a safer financial chatbot.
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.
Large language models show incredible capabilities. Learn about the technology behind them and how they came to be the powerful machines they are today.
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
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