NCA-GENL Question 160
Single answerA company plans to implement a chatbot using a large language model (LLM) to enhance customer support. The chatbot should provide quick responses based on the company's knowledge base. Which approach would best suit this use case?
- A
Train a new LLM on the company's data from scratch.
- B
Fine-tune a pre-trained LLM on the company's knowledge base.
- C
Use a Retrieval-Augmented Generation (RAG) pipeline to retrieve relevant information from the knowledge base and generate responses.
- D
Rely solely on the pre-trained LLM without any customization or retrieval mechanism.
Show answer and explanation
Correct answer: C
Explanation
Using a Retrieval-Augmented Generation (RAG) pipeline is the best approach for building a chatbot in this scenario. RAG combines the strengths of retrieval (accessing specific, updated information from the knowledge base) and generation (leveraging the LLM to generate coherent and contextually relevant responses). This ensures that the chatbot provides accurate and tailored responses without requiring exhaustive fine-tuning or retraining.
- A. Incorrect.
Training a new LLM from scratch would require extensive data, computational resources, and time, making it impractical for most use cases.
- B. Incorrect.
Fine-tuning a pre-trained LLM on the company's knowledge base can be helpful but may not be sufficient for dynamic or large knowledge bases since fine-tuning involves static updates.
- C. Correct.
Using a Retrieval-Augmented Generation (RAG) pipeline is the most suitable approach because it enables the chatbot to dynamically fetch relevant information from the knowledge base and generate accurate, context-aware responses.
- D. Incorrect.
Relying solely on a pre-trained LLM without customization or retrieval would result in responses that may lack relevance to the company's specific knowledge base.