You are deploying a chatbot using Azure OpenAI Service with a custom dataset indexed in Azure AI Search. Users report that the chatbot frequently responds with 'I don't know' for questions that the dataset should cover. What is the most likely cause?
Low confidence threshold causes relevant chunks to be excluded, leading to 'I don't know' responses.
Why this answer
Option B is correct because when the confidence threshold is set too low, Azure AI Search filters out retrieved chunks that don't meet the minimum confidence score, even if those chunks are relevant. This causes the chatbot to respond with 'I don't know' because no sufficiently confident context is passed to the Azure OpenAI model for answer generation. The issue is specifically in the retrieval configuration, not in the model's generation parameters.
Exam trap
The trap here is that candidates often confuse the confidence threshold in retrieval with the temperature parameter in generation, assuming a high temperature causes the model to refuse answers, when in fact temperature controls creativity, not retrieval filtering.
How to eliminate wrong answers
Option A is wrong because limiting the search scope to a small number of documents would reduce the pool of potential matches, but the chatbot would still return answers from those documents if they contain relevant information; the symptom of 'I don't know' for covered questions points to a filtering issue, not a scope limitation. Option C is wrong because a high temperature setting affects the randomness and creativity of the generated response, not the model's ability to retrieve or use context; it might cause verbose or off-topic answers, but not a refusal to answer. Option D is wrong because large chunk sizes can cause retrieval of irrelevant chunks due to lower precision, but this would lead to incorrect or hallucinated answers, not the model saying 'I don't know'; the model would still attempt to answer using the retrieved context.