Question: Can you recommend a tool that helps me test and evaluate my language model-based app's performance?

If you're looking for a tool to test and evaluate your language model-based app's performance, Deepchecks is an excellent choice. It automates evaluation to identify issues like hallucinations, bias, and toxic content, and uses a "Golden Set" approach for high-quality evaluation. With features like automated evaluation, LLM monitoring, debugging, and custom properties for advanced testing, Deepchecks ensures your app is reliable and of high quality throughout development and deployment.

Another robust option is HoneyHive, which offers a comprehensive platform for AI evaluation, testing, and observability. It provides a collaborative environment for testing and evaluation, with automated CI testing, production pipeline monitoring, dataset curation, and prompt management. HoneyHive supports over 100 models via integrations with popular GPU clouds and offers a free Developer plan and a customizable Enterprise plan for more advanced needs.

For those who need a flexible and customizable evaluation tool, BenchLLM might be the right fit. It allows you to create test suites for your LLM models using JSON or YAML and can integrate with APIs like OpenAI and Langchain. BenchLLM automates evaluations and produces easy-to-share quality reports, making it a valuable tool for maintaining performance in your AI applications.

Lastly, Langtail offers a suite of tools for debugging, testing, and deploying LLM prompts. It includes features like fine-tuning prompts, running tests to avoid unexpected behavior, and monitoring production performance with rich metrics. Langtail's no-code playground and adjustable parameters make it easier to develop and test AI apps collaboratively, improving team efficiency and reliability.