Question: How can I monitor and debug my LLM failures in production, is there a tool that can help me with that?

If you need tools to monitor and debug LLM failures in production, HoneyHive is a powerful choice. This platform provides a full environment for AI assessment, testing and observability. It has features like automated CI testing, production pipeline monitoring, dataset curation and prompt management that can help you debug and manage your models. HoneyHive also supports multiple models through integrations with common GPU clouds and offers multiple pricing tiers, including a free Developer plan.

Another good option is LastMile AI, which is geared to help engineers productionize generative AI applications. It comes with Auto-Eval for automated hallucination detection, a RAG Debugger for better performance tracing, and AIConfig for optimizing prompts and model parameters. The platform supports a range of AI models across multiple modalities and offers a notebook-like environment for prototyping and building apps, making it easier to deploy production-ready AI applications.

Deepchecks is another useful tool for developers building LLM applications. It automates evaluation, flagging problems like hallucinations and bias, and offers tools for version comparison and custom testing properties. That means your LLM-based software will be more reliable and of higher quality from development to deployment. Deepchecks offers several pricing levels, including a free Open-Source option.

Last, Parea offers a platform for experimentation and human annotation, which can help you debug failures and monitor performance over time. It offers tools for observability, human feedback collection and prompt management. With integrations with common LLM providers and simple SDKs for easy integration, Parea can help you capture user feedback and monitor the performance of your models in production.