AI Testing
Testing LLM-powered applications is one of the most misunderstood areas of software quality. Traditional testing relies on determinism — the same input always produces the same output. LLMs don't work that way. This guide covers the patterns that actually work. The Core Challenge: Testing Non-Determinism When you test 2
AI Testing
Every production application that calls OpenAI, Anthropic, or Google Gemini will eventually hit a rate limit. The question isn't whether you'll get a 429 error — it's whether your application handles it gracefully. Here's how to test every aspect of rate limit handling
Testing
Dynamic locale switching — changing the application language without a full page reload — is one of those features that looks simple in a demo and turns out to have a surprisingly large surface area of edge cases. Every visible element must update. User preferences must persist across sessions. Fallback chains must
AI Testing
Calling the real OpenAI, Claude, or Gemini API in every test is slow, expensive, and non-deterministic. Mocking replaces those API calls with controlled, predictable responses — making your unit tests fast and your CI pipeline cheap. Here's every mocking pattern you need. Why Mock LLM APIs Before diving in:
AI Testing
Google's Gemini API powers everything from simple text generation to multimodal applications that process images, audio, and documents. Testing Gemini-powered apps requires handling non-deterministic outputs, managing API costs, and validating complex multimodal behaviors. Here's a complete testing framework. Gemini SDK Overview Google provides two ways to
AI Testing
Testing AI APIs requires a different mindset than testing traditional REST endpoints. The Claude API from Anthropic returns probabilistic outputs — the same input can yield different responses. This guide shows you how to build a reliable test suite for applications built on the Claude API. Why Claude API Testing Is
Testing
Number formatting is one of those areas where bugs are invisible to developers working in their native locale and catastrophic to users everywhere else. The number 1234567.89 is rendered as 1,234,567.89 in the United States, 1.234.567,89 in Germany, 1 234 567,89 in
Testing
Dates and times are where internationalization bugs go to hide. They lurk in timezone conversions, emerge during daylight saving time transitions, and surface in calendar format mismatches that no developer's local test environment ever triggers. This guide covers the full scope of date and time testing for applications
Testing
Testing rate limiting is deceptively hard. The implementation often works correctly in isolation but fails under concurrent load, leaks when clients use different keys that should share a limit, or applies the wrong limit tier to authenticated users. Rate limiting testing requires concurrent request generation, careful timing, and validation of
Testing
API backward compatibility testing is one of the most critical — and most overlooked — disciplines in microservices engineering. A single breaking change pushed to a shared API can cascade into production failures across a dozen downstream services, and the failure often surfaces hours after the deployment in a way that'
Testing
Testing data isolation in multi-tenant microservices is a security-critical requirement that most teams under-test. A failure in tenant isolation — where tenant A can read or modify tenant B's data — is not just a bug. It is a data breach. It triggers regulatory notification requirements under GDPR, SOC 2,
Testing
Testing bulkhead patterns and circuit breakers is something most teams skip until an outage forces the conversation. The patterns themselves — isolating thread pools so one slow dependency can't starve another, tripping a circuit breaker so you stop hammering a dead service — are well understood conceptually. But verifying that