AIF-C01 Question 111
Single answerA company wants to build a conversational AI application that can provide accurate and context-aware answers by referencing its internal knowledge base. Which approach would be most suitable to achieve this using Retrieval Augmented Generation (RAG)?
- A
Train a large language model (LLM) exclusively on the company's entire knowledge base without further enhancement.
- B
Use a Retrieval Augmented Generation (RAG) workflow that retrieves relevant documents from the company's knowledge base and combines them with a pre-trained LLM for response generation.
- C
Implement a rule-based chatbot system with hardcoded responses for each use case in the knowledge base.
- D
Use a standalone pre-trained LLM without referencing external data sources during query processing.
Show answer and explanation
Correct answer: B
Explanation
Retrieval Augmented Generation (RAG) enhances the capabilities of pre-trained language models by incorporating a retrieval step that fetches relevant context or documents from an external knowledge base. This approach is particularly suitable for applications requiring dynamic and accurate responses based on a proprietary dataset or knowledge base. By leveraging the RAG framework, the company can combine the power of pre-trained LLMs with its internal knowledge base to provide high-quality, context-aware answers.
- A. Incorrect.
Training a large language model (LLM) exclusively on the company's knowledge base would require significant resources, time, and expertise. Additionally, it lacks the dynamic retrieval capability that RAG offers.
- B. Correct.
RAG integrates a retrieval system to fetch relevant documents from the knowledge base and combines them with a pre-trained LLM. This makes it highly effective for generating accurate and context-aware responses dynamically.
- C. Incorrect.
A rule-based chatbot system is rigid and lacks the flexibility and contextual understanding provided by models utilizing the RAG framework.
- D. Incorrect.
A standalone pre-trained LLM can produce generic responses but lacks the ability to reference the company's specific knowledge base, which is critical for accurate and tailored answers.