Why does an FAQ generator struggle to address ambiguous user queries

Question

Can you tell me Why does an FAQ generator struggle to address ambiguous user queries?

Answer 1

An FAQ generator struggles with ambiguous queries due to weak intent classification, lack of contextual memory, absence of disambiguation prompts, and inadequate confidence-based response filtering.

Here is the code snippet you can refer to:

​In the above code we are using the following key points:

• Fuzzy Matching for Closest FAQs: Uses fuzzywuzzy to match ambiguous queries.
• Confidence Thresholding: Ensures accuracy before selecting an FAQ response.
• AI-Based Disambiguation: Uses a pre-trained NLP model for fallback handling.
• Clarification Prompts: Asks users for more details if no strong match is found.
• Context-Aware Answering: Provides AI responses based on pre-existing FAQs.

Hence, resolving ambiguity in an FAQ generator requires intent-aware fuzzy matching, confidence-based filtering, AI-driven fallback responses, and interactive user clarification to ensure accurate and contextually relevant answers.