AWS AI Practitioner
A company maintains a large product catalog with detailed descriptions. The company wants to build an AI assistant to answer customer questions about the products. However, the company's labeled training data is limited. Which solution will meet these requirements with the LEAST implementation effort?
A
Use a Retrieval Augmented Generation (RAG) architecture to query the product database at runtime. Provide relevant, unmodified product descriptions as context for the foundation model (FM).
✓ Correcta
B
Fine-tune a foundation model (FM) on the restricted labeled data. Automatically refresh the model with novel product details weekly for accuracy.
C
Deploy a foundation model (FM) for each product category. Implement a routing layer to direct customer queries to the appropriate specialized model.
D
Create a new custom foundation model (FM) that is trained on the product database. Optimize the FM for minimal token usage during inference.
Explicación
RAG with the product catalog requires the least implementation effort because it doesn't require model training or fine-tuning. The product descriptions are retrieved dynamically at query time and provided as context to the FM, allowing accurate product-specific answers without labeled training data.