Domain 3: Applications of Foundation Models
Task Statement 3.1: Describe design considerations for applications that use foundation models.
Objectives:
- Identify selection criteria to choose pre-trained models (for example, cost, modality, latency, multi-lingual, model size, model complexity, customization, input/output length).
- Understand the effect of inference parameters on model responses (for example, temperature, input/output length).
- Define Retrieval Augmented Generation (RAG) and describe its business applications (for example, Amazon Bedrock, knowledge base).
- Identify AWS services that help store embeddings within vector databases (for example, Amazon OpenSearch Service, Amazon Aurora, Amazon Neptune, Amazon DocumentDB [with MongoDB compatibility], Amazon RDS for PostgreSQL).