Question: How can I quickly identify and fix issues like hallucinations, wrong answers, and bias in my LLM app?

If you need a powerful tool to quickly detect and correct hallucinations, wrong answers and bias in your LLM app, Deepchecks is a good option. It automates testing, detects problems and corrects them through a "Golden Set" approach. Deepchecks also includes automated testing, LLM monitoring, debugging, version comparison and custom properties for more advanced testing. It's geared for developers and teams who want to build top-quality LLM apps.

Another good option is LangWatch, which helps you ensure the quality and safety of generative AI products. It helps you avoid problems like jailbreaking, sensitive data exposure and hallucinations. LangWatch offers real-time metrics for conversion rates, output quality and user feedback, and tools to assess model performance and generate test data. It's geared for developers and product managers who want to ensure quality and performance.

For a broader debugging and testing suite, look at Langtail. It offers tools for debugging, testing and deploying LLM prompts, including fine-tuning prompts with variables, running tests to ensure you don't get unexpected app behavior, and monitoring production performance with rich metrics. Langtail also includes a no-code playground for writing and running prompts so you can more easily develop and test AI apps.

Last, Langfuse is an open-source platform for LLM engineering. It includes features like tracing, prompt management, evaluation, analytics and a playground for experimentation. Langfuse supports integrations with different SDKs and has security certifications like SOC 2 Type II and ISO 27001 so your data is protected. It's geared for teams that need a more complete solution for debugging and analyzing their LLM apps.