Sharing & Collaboration

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Collaborate and analyze

Once you have completed the evaluation and obtained the evaluation report, sharing the results is crucial for collaboration and decision-making.

1. Interpret the Evaluation Report: Analyze the report for patterns, trends, and insights in the app variants and models.
2. Save the Evaluation Run: Ensure you save the evaluation run within HoneyHive for future reference.
3. Add comments: Quickly add comments highlighting key findings, strengths, and weaknesses across app versions.
4. Share the Evaluation Report: Share results with the development team, product managers, AI experts, security and privacy specialists, domain experts, and end users, as appropriate.
5. Ask for Feedback: Encourage domain experts to provide their own feedback on each completion (using 👍 or 👎) to help you better understand performance and correlation with your pre-defined metrics.
6. Iterate and Reevaluate: Use the insights to refine app variants, models, and evaluation methodologies for continuous improvement.

By sharing evaluation results and collaborating with stakeholders, you can make informed decisions to enhance your LLM app’s performance, security, and user experience.

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Sharing options

You can quickly share evaluation reports to with your colleagues via these two methods:-

1. Share via Email: Opt to share the report through email for direct communication and easy reference.
2. Share Link: Generate a shareable link that allows others to access the evaluation report, streamlining information dissemination.

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What’s next

With evaluation results in hand, you’re now ready to delve deeper into analyzing model performance in a production environment. Here are some suggested next steps:

1. Production Monitoring: Instrument your application to track your model’s performance and behavior in real-world scenarios.
2. Data Driven Insights: Leverage production data to gain insights into how the model interacts with actual user inputs and optimize its responses.
3. Error Analysis: Investigate and address any discrepancies between expected and actual model outputs, refining the model’s capabilities over time.
4. Capture User Feedback: Actively incorporate user feedback loops within your application to continuously fine-tune the model’s responses and adapt to evolving user needs.
5. At-scale Optimization: As your model operates in production, explore opportunities for fine-tuning and optimization to further improve performance or lower cost.