NCA-GENL Question 162
Single answerAn enterprise is looking to implement a generative AI solution to help its customer support team handle user inquiries more efficiently. They want the model to provide accurate responses by retrieving relevant knowledge from their internal database of FAQs and product documentation. Which approach is most suitable for this use case?
- A
Retrieval-Augmented Generation (RAG)
- B
Training a chatbot from scratch with the company's data
- C
Using a summarizer to condense the internal database into a smaller dataset
- D
Fine-tuning a pretrained LLM on the internal database without retrieval
Show answer and explanation
Correct answer: A
Explanation
The RAG framework is ideal for this scenario because it enables the LLM to retrieve relevant, up-to-date information from the company's internal database in real-time, ensuring that responses are both accurate and contextually appropriate. This makes it a practical and efficient solution for customer support use cases.
- A. Correct.
Retrieval-Augmented Generation (RAG) is the most suitable approach because it combines an LLM's ability to generate human-like responses with real-time retrieval of accurate, domain-specific knowledge from external sources like the company's database.
- B. Incorrect.
Training a chatbot from scratch is time-consuming, resource-intensive, and unnecessary when existing LLMs can be enhanced with retrieval mechanisms.
- C. Incorrect.
Using a summarizer condenses content but does not address the need for dynamic retrieval of relevant responses during a live interaction.
- D. Incorrect.
Fine-tuning a pretrained LLM without retrieval might improve domain specificity but lacks the ability to dynamically pull up-to-date information from the internal database, making it less effective.