AWS Certified AI Practitioner AIF-C01 Practice Question

Retrieval-Augmented Generation (RAG) stores document embeddings in a vector database, retrieves the most relevant passages at query time, and injects them into the prompt sent to the foundation model. Because the model itself is not retrained, the process is faster and cheaper than full fine-tuning, pre-training, or collecting Reinforcement Learning from Human Feedback (RLHF) data. Fine-tuning or RLHF would require additional training cycles and do not automatically incorporate fresh content, while pre-training a new model would be far more costly and unnecessary for this scenario.