AI-powered testing guides — how to use AI to generate, run, and maintain automated tests faster.
AI Testing
LLM hallucinations — confidently stated falsehoods — are one of the hardest failure modes to catch because they look identical to correct outputs. This guide covers the practical techniques developers use to detect hallucinations in production systems and reduce their frequency. Key Takeaways Hallucinations are silent failures. A crashed request is easy
AI Testing
Testing LLM outputs is different from testing deterministic software. Outputs vary across runs, so you need a combination of rule-based checks, semantic similarity, and LLM-as-judge techniques. This guide walks through practical methods developers use to validate AI-generated text in production. Key Takeaways LLM outputs are probabilistic — plan your tests accordingly.
Testing
Amazon Kiro is the most interesting AI IDE to launch since Cursor — and it has a genuinely different approach to code generation that changes how testing needs to work. Where Cursor and Cline take a prompt-and-iterate approach, Kiro is spec-driven: you describe a feature in plain English, it generates structured
Testing
Cline has 4 million VS Code installs and is growing fast. It scaffolds entire features, runs terminal commands, and iterates on failures — which means it also generates a lot of code that needs testing. This guide covers how to test applications built with Cline: what kinds of bugs Cline code
AI Testing
Bolt.new just built you a full-stack app in 20 minutes. It looks right. The preview works. You're about to share the URL. Before you do: have you actually tested it? Not "does it load" — that's not a test. Have you verified that the
AI Testing
You've built a CrewAI crew. Three agents, two tasks, one final output. You ran it locally, the output looked correct, and you're ready to ship. Two weeks later, a user reports the output is wrong. You run it again and it's fine. You run
AI Testing
You built an AI app with the Vercel AI SDK. Streaming works. The chat UI looks right in development. You deploy to Vercel and it works in your quick manual test. Then a user hits a path where the streaming response stalls, your tool call returns data the model doesn&
AI Testing
You prompted v0.dev, got a polished React component in 30 seconds, copied it into your Next.js app, and it looked great. You shipped it. Three days later a user fills out the contact form and nothing happens. The button submits, the loading spinner appears, and then — nothing. No
AI Testing
You built a LangGraph agent. The nodes look right. The graph compiles. You run it manually and it works. Then you ship it, and a user hits an edge case in your routing logic, your agent ends up in an infinite loop, and your production system starts burning tokens until
AI Testing
Testing an AI chatbot is fundamentally different from testing regular software. There's no deterministic output. The same input can produce different responses. The failure mode isn't a crash or a 500 error — it's a confident wrong answer, a hallucinated fact, a response that violates
AI Testing
Amazon released the Strands Agents SDK in May 2025. If your team is already deep in the AWS ecosystem — Bedrock models, Lambda, DynamoDB — Strands is the natural path to production AI agents. It integrates cleanly with AWS services and handles the orchestration layer for you. But clean integration and correct
AI Testing
smolagents is HuggingFace's lightweight Python library for building agents. "Small" is in the name — the library is minimal by design. What that means for testing: you get less scaffolding to hide behind and more direct access to the agent's internals. That's actually