Beyond Unit Tests Build reliable AI products by testing models, prompts, data, workflows, and production behavior together Traditional unit tests are important, but they cannot fully validate an AI-native...
Deploying Foundation Models Fast on AWS How SageMaker JumpStart simplifies model discovery, deployment, customization, and evaluation for production ML workloads Deploying a foundation model can involve model selection, infrastructure...
Keep AI-assisted development aligned with the original feature goal from requirements through implementation AI coding tools can move quickly from a simple feature request to changes that were never...
A practical troubleshooting guide for file patterns, workspace roots, hook versions, enabled states, and trigger behavior Kiro Agent Hooks can automate tasks when files are created, saved, or deleted,...
A practical guide to writing clear, testable requirements that Kiro can turn into better designs and implementation tasks Kiro uses requirements.md as the starting point for its Spec workflow,...
Move your existing Amazon Q Developer workflow to Kiro with minimal disruption, while preserving your tools, settings, & development habits Migrating from Amazon Q Developer to Kiro is more...
A practical framework for monitoring AI quality, detecting performance changes, and responding before users notice a problem. AI systems can degrade without any obvious infrastructure failure. A model may...
Build an AI cost model early so your product can grow without letting inference, infrastructure, and usage costs outpace revenue. AI features can look inexpensive during development and become...
A practical framework for choosing AI models based on task complexity, response time, accuracy, cost, and the actual needs of your users. Choosing an AI model is not simply...
Build a practical AI product team using existing engineering skills, clear ownership, and targeted specialist support. Building an AI native product does not automatically mean hiring machine learning researchers,...