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 framework
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 to
Testing
Bad alerting is worse than no alerting. A monitoring setup that pages you at 3am for a 2-second blip that self-resolved trains your team to ignore alerts. And teams that ignore alerts miss the real outages. Getting synthetic monitoring alerting right is mostly about restraint: being precise about what constitutes
Testing
Synthetic monitoring is one of those terms that sounds more complicated than it is. At its core, it means running scripted tests against your application on a schedule — simulating what a real user would do — and alerting you when something breaks. No real users required. If you've ever
Testing
Every mature test strategy uses multiple types of tests, each serving a different purpose. Smoke, regression, and acceptance testing are three of the most commonly used — and most commonly confused. They're not interchangeable. Running the wrong type of test at the wrong time wastes time at best and
Testing
Smoke testing and sanity testing get conflated constantly. Both are fast, both happen early in the testing cycle, and both catch obvious problems before you waste time on deeper testing. But they serve different purposes, run at different times, and cover different ground. Mixing them up leads to gaps in
Testing
Playwright is a strong choice for smoke testing. It's fast, handles modern web apps well, and has built-in support for parallel execution, auth state persistence, and multiple browsers. This post walks through building a complete smoke test suite with Playwright, from project setup to CI integration. Project Setup
Testing
A deployment pipeline without smoke tests is like a fire sprinkler system with no water pressure test. Everything looks fine until the moment you need it. Smoke tests in CI/CD serve one purpose: stop bad builds before they cause damage. If the app doesn't boot, a critical
Testing
Testing React components has a reputation for being awkward. Shallow rendering, mock components, snapshot tests that break on every change — these patterns produce test suites that slow teams down rather than help them. React Testing Library changed this by enforcing a simple rule: test components the way users use them,
Testing
Test-Driven Development has a reputation for being a discipline that sounds great in theory but falls apart under deadline pressure. That reputation is wrong, and the reason is usually a misunderstanding of what TDD actually is. TDD is not "write all your tests before you write any code."
Testing
TDD and BDD are often mentioned in the same breath, sometimes used interchangeably, and frequently confused with each other. Both are test-first development disciplines. Both produce better software than writing tests after the fact. But they operate at different levels, involve different audiences, and solve different problems. Using the wrong
Testing
Python and pytest are a natural fit for TDD. pytest has minimal ceremony, excellent error messages, and a parametrize decorator that makes covering edge cases straightforward. This tutorial builds an order pricing engine from scratch using strict red-green-refactor discipline. No implementation code gets written until a test demands it. Setup