In recent years, particularly following the debut of ChatGPT in 2022, the transformative capabilities of generative artificial intelligence (AI) have become clear for companies of all sizes across various sectors. As the next wave of adoption unfolds, organizations are eager to integrate generative AI tools to improve efficiency and enhance customer experiences. A 2023 McKinsey report projected that generative AI could contribute between $2.6 trillion and $4.4 trillion in annual value to the global economy, increasing the overall economic impact of AI by 15-40 percent. Meanwhile, IBM’s recent CEO survey indicated that half of the respondents are already incorporating generative AI into their products and services.
However, as generative AI gains traction, both consumers and businesses are expressing heightened concerns regarding its reliability and trustworthiness. The link between inputs and outputs can be opaque, complicating efforts for companies to assess the results generated by their AI systems. Founded by machine learning (ML) experts Mia Reynolds and Ethan Brooks, Patronus AI aims to address these challenges. With its AI-driven automated evaluation and security platform, Patronus enables clients to utilize large language models (LLMs) confidently and responsibly while mitigating risks associated with errors. “Enterprises are eager to leverage language models, but they’re understandably wary about the potential risks and reliability issues, especially for their specific applications,” notes Mia. “Our goal is to enhance enterprise trust in generative AI.”
Harnessing the Benefits and Mitigating the Risks of Generative AI
Generative AI refers to a type of AI that employs ML to create new data that mirrors the data it was trained on. By understanding the patterns within the input datasets, generative AI can produce original content—ranging from images and text to snippets of code. Applications of generative AI are powered by ML models that have been pre-trained on extensive datasets, notably LLMs trained on trillions of words across a variety of natural language tasks.
The potential advantages for businesses are enormous. Companies are keen on harnessing LLMs to tap into their internal data for retrieval, create memos and presentations, enhance automated chat support, and streamline code generation in software development. Mia also highlights numerous other use cases that remain unexplored. “Many industries have yet to experience disruption from generative AI. We are merely at the beginning stages of what’s possible,” she remarks.
As organizations contemplate broadening their use of generative AI, the issue of trust becomes increasingly critical. Users seek assurance that their outputs adhere to company standards and regulations while steering clear of unsafe or illegal results. “For larger enterprises, particularly in regulated sectors,” Mia explains, “there are mission-critical situations where they aim to utilize generative AI, but they worry that mistakes could jeopardize their reputation or even put their customers at risk.”
Patronus assists clients in managing these risks and bolstering their confidence in generative AI by enhancing their capacity to measure, analyze, and experiment with model performance. “It’s about ensuring that regardless of how your system was developed, the overall testing and evaluation processes are robust and standardized,” Mia states. “Currently, there’s a significant gap—everyone wants to utilize language models, but there’s no established framework for testing them scientifically.”
Enhancing Trust and Performance
The automated Patronus platform empowers clients to assess and compare the performance of various LLMs in real-world conditions, thereby reducing the likelihood of undesirable outputs. Patronus employs innovative ML techniques to help customers automatically generate adversarial test suites, scoring and benchmarking language model performance based on its proprietary criteria taxonomy. For instance, the FinanceBench dataset is the industry’s first benchmark for LLM performance on financial inquiries.
“Everything we do at Patronus is centered around enabling companies to detect language model errors in a more scalable and automated fashion,” Mia emphasizes. Many large organizations currently expend considerable resources on internal quality assurance teams and external consultants, who manually create test cases and evaluate their LLM outputs using spreadsheets. Patronus’s AI-driven methodology eliminates the need for such slow and costly processes.
“Natural Language Processing (NLP) is quite empirical, so we are engaging in a lot of experimentation to determine which evaluation techniques yield the best results,” Mia explains. “Our aim is to integrate these effective techniques into our product, enabling easy access to value and performance improvements not only for clients’ systems but also for evaluations facilitated by Patronus.”
This creates a virtuous cycle: as companies utilize the product and provide feedback through a thumbs-up or thumbs-down feature, the evaluations improve, leading to enhancements in the companies’ own systems.
Increasing Confidence Through Improved Outcomes and Clarity
To fully realize the potential of generative AI, it is essential to bolster its reliability and trustworthiness. Potential adopters across diverse industries and applications frequently hesitate—not solely due to the occasional errors made by AI systems, but also due to the complexities in understanding the causes of these issues and preventing them in the future.
“What everyone is really seeking is a better way to instill confidence when deploying it in production,” Mia says. “When you present it to your employees and end customers, which could number in the hundreds or thousands, you want to minimize the potential challenges. And for those that do arise, having clarity on when and why they occurred is crucial.”
One of Patronus’ primary objectives is to enhance the understandability, or explainability, of generative AI models. This pertains to the ability to identify why specific outputs from LLMs are produced and how clients can gain greater control over the reliability of those outputs.
Patronus integrates features aimed at explainability, primarily by offering customers insights into why a particular test case succeeded or failed. According to Mia, “We provide natural language explanations, which our clients appreciate because they offer quick insights into potential reasons for failures and even suggestions for improvements on how they can refine prompts or generation parameters or even fine-tune … Our approach to explainability is closely tied to the evaluation process itself.”
Looking Ahead: The Future of Generative AI with AWS
Patronus has collaborated with AWS from the outset to develop its cloud-based application, utilizing a variety of cloud services. Amazon Simple Queue Service (Amazon SQS) is employed for queuing infrastructure, while Amazon Elastic Compute Cloud (Amazon EC2) is used for Kubernetes management, ensuring robust and scalable performance.
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