AWS Certified AI Practitioner AIF-C01 Practice Question

Retrieval-Augmented Generation (RAG) works by first searching a designated knowledge source (such as the company's document repository), retrieving the most relevant passages, and then supplying those passages to the foundation model as additional context. Because the model receives up-to-date content at inference time, it can generate responses grounded in the company's current HR policies without the cost and complexity of pre-training or full fine-tuning.

Increasing temperature only affects randomness and does not introduce company-specific knowledge. Pre-training a new model from scratch and fully fine-tuning an existing model would embed the data, but both are far more expensive and time-consuming than RAG, and they require redeployment each time the documents change.