Question 1

What is Amazon Bedrock?

Accepted Answer

Amazon Bedrock is a fully managed AWS service that gives access to foundation models from Amazon (Nova), Anthropic, Meta, and others through a single API. It is used to build generative AI applications, RAG pipelines, and AI agents on AWS without managing model infrastructure, and it includes guardrails, knowledge bases, and agent features.

Question 2

How do you build a GenAI application on AWS?

Accepted Answer

You build a GenAI application on AWS by selecting a foundation model on Amazon Bedrock, grounding it on your own data with retrieval-augmented generation (RAG) using a vector store, adding guardrails for safety, and exposing it through AWS Lambda or a container behind an API. For agentic behavior you add tool use and the Model Context Protocol, with IAM controlling what the model can access.

Question 3

What is RAG and when should you use it on AWS?

Accepted Answer

RAG (retrieval-augmented generation) feeds a model relevant passages from your own documents at query time so answers are grounded in your data instead of only the model's training. Use it on AWS when answers must reflect private, current, or domain-specific content; it is commonly implemented with Amazon Bedrock Knowledge Bases or a vector database plus an embedding model.

Question 4

How do you keep AWS GenAI costs under control?

Accepted Answer

Keep AWS GenAI costs down by choosing a right-sized model for each task, limiting input and output tokens, caching repeated prompts and retrievals, batching where possible, and monitoring spend with cost tracking and alerts. Picking a smaller model that is good enough, rather than the largest available, is usually the biggest single saving.

AWS AI and GenAI

AWS AI & GenAI articles (19)