Foundation Model Integration, Data Management, and Compliance I
Learn to design efficient and compliant GenAI solutions by integrating foundation models with scalable vector stores, applying precise retrieval methods, and ensuring high availability with automatic failover. This lesson helps you optimize data ingestion, metadata filtering, and real-time model switching to meet strict latency and accuracy requirements.
We'll cover the following...
Question 1
A logistics company is building a GenAI assistant to answer questions. The company has over 18 million shipping documents stored in Amazon S3. New documents arrive continuously, averaging 30,000 updates per hour.
Requirements:
Sub-300 ms p95 retrieval latency.
Incremental ingestion without re-embedding existing data.
Metadata filtering by region and document type.
Minimal operational overhead.
The team is evaluating vector store options.
Which solution best satisfies ...