Amazon Bedrock Agents automate workflows by giving foundation models the ability to call tools (APIs, Lambda, databases). This guide covers building agents with tool definitions, testing in the console, handling errors, and scaling to production.
Amazon Bedrock Knowledge Bases automate the RAG (Retrieval-Augmented Generation) pipeline — semantic search, chunking, embedding, and context injection into Claude or other foundation models. This guide covers setup, data ingestion, cost optimization, and production patterns.
Amazon SageMaker automates ML training, but instance costs add up fast. This guide covers spot instances, instance selection, distributed training, and production patterns to reduce SageMaker costs by 50-70%.
Amazon Bedrock Guardrails protect foundation models from harmful outputs — filtering on prompt injection, jailbreaks, toxicity, and PII. This guide covers setup, testing, cost optimization, and production safety patterns for GenAI applications.
Amazon Q for Business is a generative AI assistant for enterprise search and document retrieval. This guide covers setup with SharePoint and S3 data sources, user management, and production deployment patterns.
Deploying GenAI without guardrails is a compliance incident waiting to happen. Here is how to build a production-grade AI governance layer on AWS using Amazon Bedrock Guardrails, least-privilege IAM, and continuous evaluation.
A practical comparison of Amazon Q for Business and ChatGPT Enterprise for enterprise AI assistants — covering data security, integrations, cost, and deployment models.
Building generative AI on AWS? Amazon Bedrock removes the complexity of training and hosting foundation models, letting businesses deploy production LLM apps faster, more securely, and at lower cost.