Data Interview Question

FAQ Response System

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Requirements Clarification & Assessment

  1. Objective:

    • Develop a chatbot capable of responding to user queries by matching them with the most relevant FAQ from a predefined list.

  2. Input/Output:

    • Input: A user query in natural language.
    • Output: The most relevant FAQ answer or a default response directing to customer support if no relevant FAQ is found.

  3. Constraints:

    • Accuracy: The system should accurately match queries to FAQs with high precision.
    • Scalability: The system should handle a large number of FAQs and queries efficiently.
    • Real-time Performance: Responses should be generated in real-time or near-real-time.

  4. Data Availability:

    • FAQ Dataset: A list of FAQs with corresponding answers.
    • Historical Queries: Past user queries and their matched FAQs, if available, for supervised learning.

  5. Evaluation Metrics:

    • Precision & Recall: To measure the accuracy of the FAQ matching.
    • Response Time: To ensure real-time performance.
    • User Satisfaction: Feedback from users to assess the quality of responses.

  6. Fallback Mechanism:

    • A mechanism to route the query to a human agent if no suitable FAQ is found.