Last updated: September 16, 2024 at 07:41 PM
Summary of Reddit Comments on LLM evaluation tool
General Interest in Helping with LLM evaluation tool
- Many users express their willingness to help with the LLM evaluation tool.
- One user mentions they had a tough time choosing an LLM recently.
- Another user states, "LLM evaluation is a major pain point for me right now so interested in any solutions."
User Comments on Tool Features and Design
- A user praises the tool's swagger doc and asks for more details on the execution graph layout.
- Another user appreciates the UI and workflow design, seeking clarification on how intermediate steps are implemented.
- A user mentions the tool being featured in PyCoder's Weekly and shares a proper project description.
- There is a mention of PyCoder's Weekly featuring the project.
Community Participation and Engagement
- Some users express interest in participating in an LLM session with the community.
- A user shares a resource about GPT and discusses the need for separate metrics for Completeness and Relevance.
Feedback on Existing Tools and Suggestions
- Users discuss various tools like Fabric, Promptmage, Langsmith, and Make.com for creating APIs and working with custom models.
- A user inquires about Honeyhive AI and receives recommendations for Magu.ai, Langtrace.ai, and Prompteams.com.
Questions and Feedback on Tool Functionality
- Users ask about different aspects of the tool such as its utility for production or experimentation, LLM-based evaluation mechanism, and comparing applications with different options.
- A user raises a query about the DB setup and open telemetry compatibility of the tool.
Critique and UI Suggestions
- A user provides a UI critique regarding tag alignment in an image.
- Another user asks about the advantage of the tool over Langfuse and suggests comparing it to their own tool.
Miscellaneous Comments
- Users leave positive feedback, express excitement to try the tool, and appreciate the effort put into sharing resources.
- A user requests a reminder for future follow-up on the tool.
Overall, the LLM evaluation tool has drawn interest from users in the community, with discussions ranging from features, functionality, comparisons with other tools, and potential use cases. There is a mix of positive feedback, questions, and suggestions for improvement.