If AI is generating irrelevant responses that do not match customer questions, what should be adjusted?

In cases where AI is producing irrelevant responses that do not align with customer inquiries, it is essential to consider multiple factors that might contribute to this issue. Refining the AI training data can enhance intent recognition, meaning the AI would be better at understanding and interpreting the user's questions in a contextually appropriate way. This is critical because accurate intent recognition is foundational to providing relevant responses.

Updating prompt templates serves to improve grounding by ensuring that the AI has a better framework for responding to different types of queries. Well-structured prompts lead the AI towards more accurate and context-aware outputs.

Additionally, checking the retrievers for incorrect data is vital; if the AI's underlying mechanisms for fetching information are flawed, it won’t matter how refined the training data or prompts are—irrelevant data will still result in irrelevant responses.

By addressing all these aspects—training data, prompt structures, and retrieval accuracy—you can holistically improve the AI system, ensuring that it provides more relevant and useful responses to customer inquiries. This comprehensive approach is why selecting the option referencing all areas is appropriate.