Go from Generative AI Idea to Product Faster with Verta GenAI Workbench

When it comes to generative AI, or GenAI, achieving optimal results akin to traditional machine learning, or ML, methodologies demands a considerable investment in experimentation. GenAI systems, often based on architectures like GPT, rely on learned patterns and probabilistic generation. This introduces a level of complexity where the quality of outcomes is intricately linked to the input prompts and the chosen model configuration. Because of this, practitioners engaged in the deployment of GenAI must iterate through various combinations of input instructions and model architectures to discern the most effective configurations for their specific use cases.

The trial-and-error process in GenAI extends beyond merely selecting appropriate prompts and models, though. It encompasses understanding the nuanced interplay between input parameters, training data and the desired outcomes. The generative nature of these models implies a certain level of unpredictability and creativity. Therefore, it is imperative for developers to experiment with diverse approaches to guide the AI toward producing desired and contextually relevant outputs.

Of course, that is easier said than done. But wait, don't fret. Verta, the AI model management and operations company, will take over the heavy lifting with the Verta GenAI Workbench, an all-in-one platform to accelerate the GenAI builder’s journey from idea to product.

The purpose of the workbench is to allow builders to experiment with and refine custom LLM solutions for their GenAI applications. The workbench supports an always up-to-date variety of open-source LLMs like Llama 2, Mistral and Falcon alongside proprietary models like OpenAI’s GPT models and Google’s PaLM. It leverages prompting techniques and GenAI itself to create “Starter Kits” with strong baseline prompts for various use cases.

What also sets the workbench apart from competitors is the GenAI Leaderboard, which ranks model and prompt configurations on different dimensions like application-specific quality, cost, and latency, enabling end users to pick the right solution for their specific needs. This also allows non-technical domain experts to steer the results of the application, during product development and also after launch.

The result of these capabilities? A single platform that can help builders easily choose and, when needed, switch between different providers and prompts without negatively impacting their GenAI application.

“The GenAI product development process today is a lot like the Gartner hype cycle: teams rapidly enter a painful valley of disenchantment when they attempt to make the use case work consistently, with high quality and at reasonable cost,” said Verta CEO Manasi Vartak. “Exiting the valley requires a combination of smart prompting, standing up alternative open-source and proprietary models, data labeling and infrastructure building. We built the Verta GenAI Workbench to help teams go from GenAI hype to GenAI productivity as quickly as possible.”

The Verta GenAI Workbench is available on AWS, Azure and GCP, as well as on-premise deployments.