Hello everyone,
I’ve built an AI assistant using OpenAI’s platform, and I’m currently capturing prompts and responses to debug its behavior. So far, I’ve been logging input prompts and assistant output, but I want to take this further.
I’m interested in learning about methods, frameworks, or tools others are using to evaluate and improve assistant behavior. Specifically:
How do you quantify response quality (accuracy, tone, relevance, etc.)?
Do you use any kind of automated grading or eval pipelines (e.g., OpenAI Evals, LangSmith, or Humanloop)?
How do you visualize and track conversation quality over time?
Would appreciate pointers, workflows, or even your own setup examples.
NOTE: I’m also using a RAG (Retrieval-Augmented Generation) to improve contextual a…
Hello everyone,
I’ve built an AI assistant using OpenAI’s platform, and I’m currently capturing prompts and responses to debug its behavior. So far, I’ve been logging input prompts and assistant output, but I want to take this further.
I’m interested in learning about methods, frameworks, or tools others are using to evaluate and improve assistant behavior. Specifically:
How do you quantify response quality (accuracy, tone, relevance, etc.)?
Do you use any kind of automated grading or eval pipelines (e.g., OpenAI Evals, LangSmith, or Humanloop)?
How do you visualize and track conversation quality over time?
Would appreciate pointers, workflows, or even your own setup examples.
NOTE: I’m also using a RAG (Retrieval-Augmented Generation) to improve contextual accuracy.