AI-powered testing guides — how to use AI to generate, run, and maintain automated tests faster.
Testing
Your RAG system passes all tests in staging. You deploy to production. Three weeks later, users start complaining that answers are wrong. You check the code — nothing changed. You look at the LLM — same model, same prompts. But something degraded. What actually happened: the knowledge base grew, old documents became
RAG
RAG (Retrieval-Augmented Generation) systems fail in ways that are invisible without the right tests. The LLM might generate confident-sounding output that's completely ungrounded in the retrieved documents. The retriever might fetch irrelevant documents. The response might be faithful to the retrieved context but that context might
Testing
Chunking is one of the most consequential decisions in any RAG system, and one of the least tested. Most teams pick a strategy, set a chunk size, and ship. Then they wonder why retrieval quality degrades on longer documents, or why the system can never answer questions that span multiple
AI Testing
Computer vision models break in ways that unit tests don't catch. Your preprocessing pipeline silently changes a pixel value range, your bounding box coordinates are in the wrong format, or your model returns detections in a different order than your downstream code expects. None of these show up
AI Testing
CodeRabbit adds AI-powered code review to your pull requests. It comments on potential bugs, style violations, and security issues automatically. But here's the thing: AI code reviewers need to be tested too. If your review automation is generating noise, missing real issues, or slowing down merges, you
Test Management
Most QMetry users stay in the basics: create test cases, build cycles, execute, export a report. That's fine for getting started. But QMetry has a set of deeper features that, once you know they exist, save significant time for teams running at scale. This post covers the advanced
AI Testing
A prompt is code. It has inputs, outputs, and behavior that can regress when you change it. Most teams don't treat it that way — they edit prompts in a text file, run a few manual checks, and ship. Then something breaks in production and nobody knows which prompt
Testing
Prompt injection is to LLM applications what SQL injection was to web applications in 2003. The pattern is structurally identical: untrusted user input is concatenated into a command string and executed by an interpreter that cannot distinguish between the developer's intended instructions and the attacker's injected
AI Testing
Traditional test automation has a cost problem: it requires skilled automation engineers, significant time investment, and ongoing maintenance.
AI Testing
Amazon Q Developer is AWS's AI coding assistant, and it has a specific advantage over general-purpose tools like Copilot: it knows AWS.
Testing
Red-team exercises produce findings. Findings produce mitigations. Mitigations are shipped in code changes.
QA
Two years ago, the question was "should we outsource QA or hire in-house?" Today, there's a third option that's changed the economics significantly: AI-powered QA services that automate test creation, execution, and monitoring without a team of human testers. This isn'