Enhance your customer experience, empower users to create and share custom copilots, and proactively identify and mitigate risks with Fuel iX by TELUS Digital. Discover how more than 50,000 users are leveraging our award-winning generative AI engine to achieve results.
A recent TELUS Digital survey explored consumer sentiment around the origin and quality of generative AI training data. Take a look at the findings.
The Supervisor LLM moderation technique uses one large language model to filter and moderate the content generated by another to prevent harmful outputs.
These 3 real-world examples show how generative AI in healthcare boosts physician productivity, lowers costs, and improves patient outcomes.
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.
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