Exam AWS Certified AI Practitioner AIF-C01 topic 1 question 98 discussion

Retrieval Augmented Generation (RAG) is a technique that combines the power of pre-trained large language models (LLMs) with a retrieval mechanism to fetch relevant documents or data (like the company’s policies) and incorporate them into the response generation process. This approach allows the model to produce accurate, context-aware responses without the need to retrain or fine-tune the model extensively on the entire policy dataset, making it a cost-effective and efficient solution.

RAG provides most cost-effective solution

The correct answer is C. RAG allows direct use of policy documents without expensive model training.

RAG (Option C) is the most cost-effective choice because it allows the LLM to dynamically retrieve relevant information from a predefined knowledge base (the company policy) at inference time, without needing extensive fine-tuning or retraining of the model. This reduces the need for costly computational resources while still providing accurate, contextual responses.