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  1. Generative AI

    RAG: How retrieval augmented generation systems work

    Learn what retrieval augmented generation (RAG) is, how it enhances generative AI like large language models (LLMs), and key considerations for RAG systems.

  2. Responsible AIBanking, Financial Services & Insurance

    Partial intent classification: How we made a safe conversational AI assistant for a financial services firm

    Intent classification used in concert with a large language model (LLM) and retrieval-augmented generation (RAG) system resulted in a safer financial chatbot.

  3. Generative AI

    Evaluating RAG: Using LLMs to automate benchmarking of retrieval augmented generation systems

    Large language models (LLMs) are effective tools for testing how well retrieval augmented generation (RAG) systems can enhance a generative AI model.

  4. Generative AIHealthcare

    Applying generative AI innovation to medical education

    See how WillowTree partners with the University of Virginia School of Medicine to explore generative AI and use case prioritization in medical education.

  5. Generative AI

    How to select a large language model

    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.

  6. Linguistic Annotation

    Natural language processing: The power behind today's large language models

    Large language models show incredible capabilities. Learn about the technology behind them and how they came to be the powerful machines they are today.

  7. Generative AI

    Evaluating truthfulness: A deeper dive into benchmarking LLM accuracy

    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.

  8. Generative AI

    OpenAI or Azure OpenAI: Can models be more deterministic depending on API?

    Our latest findings on the differences in behavior between the OpenAI and Azure OpenAI API

  9. AI Best Practices

    How to detect and mitigate machine learning model drift

    Model drift degrades the accuracy of your ML model over time. Discover some best practices for detecting and mitigating it quickly and efficiently.

  10. Generative AI

    LLM evaluation framework: How to prevent drift and system degradation

    WillowTree’s approach to benchmarking accuracy of applications with large language models in the loop.

  11. Responsible AI

    Three ways to prevent AI hallucinations

    Boost AI reliability by preventing AI hallucinations with WillowTree's three-pronged approach to minimize and mitigate incorrect information produced by LLMs.

  12. Responsible AI

    AI hallucinations: A defense-in-depth approach

    AI hallucinations are a reality of working with large language models (LLMs), but a defense-in-depth approach helps reduce generative AI hallucination rates.

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