• Galileo has launched the first-ever large language model (LLM) diagnostics and explainability platform to reduce model hallucinations.
• This platform, known as Galileo LLM Studio, provides data scientists with tools to fine-tune LLMs with proprietary data, create and manage prompts, identify potential model hallucinations and more.
• The free tools in Galileo LLM Studio – the Galileo Prompt Inspector and the Galileo LLM Debugger – help data scientists improve their models’ performance and accuracy.
Galileo Launches First Ever Large Language Model Diagnostics Platform
Business Wire announced today that Galileo has launched a suite of new tools called Galileo LLM Studio – now available for waitlist signups. This platform provides organizations of all sizes with access to tools that quickly and easily evaluate the results of these Large Language Models (LLMs) and optimize their performance.
Tools Included in the Platform
Galileo LLM Studio contains two free tools that help data scientists improve their models’ performance and accuracy: The Galileo Prompt Inspector which enables users to identify potential model hallucinations; and the Galileo LLM Debugger which allows users to fine-tune LLMs with their own proprietary data.
Benefits of Using Galileos Tools
Adapting LLMs to specific real-world applications depends on data more than ever before, so using these tools acts as a “data force multiplier” for teams. It also helps explore the semantic search space of possible inputs that resolve to accurate user intent.
Yash Sheth, co-founder & chief product officer at Galileo states that “Today, an organization’s data is its only differentiator” when it comes to using large language models effectively. Atindriyo Sanyal, co-founder & chief technology officer at Galileo adds on by stating that major factor in getting good outputs from these models is exploring possible inputs accurately .
Overall this platform gives organizations access to powerful tools they need in order make sure their AI solutions are effective while leveraging large language models efficiently.