LLMs are disrupting the legal industry
Liability disaster
This scenario is inspired by a real case where a lawyer used ChatGPT to research legal precedents, resulting in submitting a brief with fictitious cases. This led to potential sanctions and the dismissal of the lawsuit.
LLM errors and hallucinations can lead to significant risks
Get human-accurate LLM oversight with customized models
Log10 AutoFeedback evaluation models detect errors and reduce risk by instantly reviewing LLM completions with the accuracy of a human. Inspired by interpretability research and using latent space technology, Log10 AutoFeedback offers accuracy comparable to fine-tuned models and surpasses LLM-as-a-judge, all while needing far fewer samples for faster, more efficient deployment.
Log10 AutoFeedback models are faster, cheaper, and require minimal data. Zero-shot prompting is quick but often lacks accuracy, while fine-tuned models are precise but demand significant resources. Log10 AutoFeedback combines the best of both, offering accuracy comparable to fine-tuned models and surpassing LLM-as-a-judge, all while needing far fewer samples for faster, more efficient deployment.
Monitoring & Alerting
Set quality threshold targets and understand exactly how your application is performing. Get alerts when quality falls below critical thresholds.
Ranking & Triaging
Errors are automatically prioritized and queued for resolution. Engineers debug and resolve issues using the Log10 LLMOps Observability Stack.
Self-Improving Applications
Datasets curated with feedback automatically tune prompts and models, continuously improving accuracy while in production.
