Log10: Revolutionizing AI Accuracy
In the realm of AI, accuracy is of utmost importance, especially in high-stakes and regulated industries. Log10 emerges as a powerful solution to address the challenges associated with AI accuracy.
Overview
Log10 offers a comprehensive suite of tools and features designed to enhance the accuracy of AI applications. It tackles issues such as errors and hallucinations in LLMs, which can have catastrophic consequences in sectors like healthcare, finance, insurance, and law. By providing an end-to-end accuracy solution, it ensures that from the development stage to production, AI accuracy is maintained at every step.
Core Features
- Logs & Annotation: This allows for the capture of expert insight. Evaluation criteria can be created, and LLM completions can be reviewed in a streamlined inbox. Feedback from end users through the API is incorporated, and annotations flow into the Feedback Stream for automatic dataset curation.
- Evaluation: Continuous evaluation is made possible with a declarative test suite. It seamlessly utilizes platform-curated datasets to achieve accuracy targets and can be integrated with CI/CD to prevent hallucinations as models and prompts evolve.
- AutoFeedback: Log10 AutoFeedback rapidly assesses AI performance. With just a few samples, it can evaluate LLM completions with expert-level precision, scaling expert review in a fraction of the time and delivering a real-time accuracy signal that reflects the user experience.
Basic Usage
To get started with Log10, developers can spin up custom evaluations using 90% less data. By annotating just a few samples and turning on Log10 AutoFeedback, they can begin auto-grading LLM completions based on their domain criteria. This enables them to scale subject matter expert review by 10x, removing human-in-the-loop bottlenecks and allowing experts to complete their work more efficiently.
Compared to other existing AI solutions, Log10 stands out with its focus on accuracy and its ability to provide real-time insights and improvements. It offers a more streamlined and efficient approach to ensuring the reliability of AI applications, making it a valuable asset in the world of AI development and deployment.
