Testing
Most disaster recovery plans have never actually been tested. They exist as documents — carefully written, officially approved, and completely unvalidated. When an actual disaster hits, teams discover that the documented 4-hour RTO takes 14 hours, the backup restoration process is broken, and nobody has the credentials to access the
AI Testing
Deploying an LLM-powered application without safety testing is shipping code without security review. The risks are concrete: users manipulating your model to bypass restrictions, prompt injection attacks that hijack agent behavior, unintended leakage of system prompts or training data, and content policy violations that create legal or reputational exposure.
AI Testing
The most common source of production failures in LLM applications isn't a bad answer — it's a bad format. Your code expects {"sentiment": "positive", "score": 0.87} and the model returns The sentiment is positive with a confidence of 87%. JSON.
AI Testing
Measuring the quality of a large language model output is fundamentally different from measuring traditional software behavior. There is no single assert response == expected — you are comparing probabilistic text against a range of acceptable answers. Getting this right requires a layered approach: automated metrics for scale, model-based judges for
AI Testing
When a model release announces "state-of-the-art on MMLU" or "beats GPT-4 on HumanEval," how much should you care? Understanding what these benchmarks actually measure — and what they don't — is essential for making informed model selection decisions and designing your own
AI Testing
"The new prompt feels better" is not an evaluation strategy. Moving from GPT-4 to Claude, or changing a system prompt, requires rigorous A/B testing to make confident decisions — especially when the differences in quality are subtle and user impact is significant. This guide covers the statistical
AI Testing
Deploying an LLM is not a one-time event. Prompts change. Models get updated. Retrieval indexes get refreshed. Each of these changes can silently degrade the quality of your AI application — and without a continuous evaluation pipeline, you won't know until users start complaining. This guide covers how
Provar
Provar is a Salesforce-native test automation tool built specifically to address the most common pain points in Salesforce UI testing: dynamic element IDs, frequent layout changes from seasonal releases, and the complexity of Lightning Web Components. While general-purpose tools like Selenium can be made to work with Salesforce,
No-Code Testing
The no-code testing market has exploded over the past few years. What was once a category dominated by expensive enterprise tools has become a crowded space with options ranging from free open-source projects to six-figure enterprise contracts. For teams trying to evaluate their options, the sheer number
No-Code Testing
Software quality used to be the exclusive domain of engineers. If you wanted automated tests, you needed someone who could write Python, JavaScript, or Java — and ideally someone who understood Selenium's quirks, could configure CI pipelines, and was comfortable debugging stack traces at 11pm. That world is changing
No-Code Testing
If you've been manually clicking through the same five workflows before every release, you already know the problem. Manual regression testing is slow, error-prone, and doesn't scale. But every time someone mentions automated testing, the conversation turns to code — Selenium, Cypress, Playwright — and suddenly it
Playwright
Playwright has become the gold standard for browser automation. Released by Microsoft in 2020, it quickly surpassed Selenium and gave Cypress serious competition, offering faster test execution, better support for modern web patterns (shadow DOM, iframes, async operations), and a genuinely excellent developer experience. There's one problem: Playwright