From MVP to Production // Day 2 Panel 2 // AI in Production Conference

Feedback Loops

Establishing effective feedback loops is crucial for the continuous improvement of AI models. Eric Peter emphasizes the importance of involving expert stakeholders who can provide valuable insights during the evaluation process. He notes that having the right tools to capture and analyze user feedback is essential, as it allows developers to measure user satisfaction and identify areas for improvement 1.

Having the right tooling to do that is incredibly important because the most brute force way to do that is, hey, here, you can come chat with the thing, write it down in an excel spreadsheet and give me notes.

--- Eric Peter

This approach ensures that AI systems are iteratively refined based on real-world usage, enhancing their overall effectiveness and reliability.

   

Feedback Tools

The panel highlights various tools and techniques for efficiently capturing and processing user feedback. Phillip Carter shares that at Honeycomb, user feedback is integrated into their observability systems, allowing for detailed analysis of inputs and outputs 2. This integration helps identify patterns in user feedback and improve product performance.

User feedback is basically like a column on an event. And so because we have that sort of rich instrumentation, we can just slice and dice based off of user feedback.

--- Phillip Carter

Alex Volkov adds that tools like Weights & Biases provide comprehensive solutions for tracking changes from model training to production, ensuring that any modifications are effectively monitored and evaluated.