Testing
Testing message ordering and exactly-once delivery in event-driven systems is one of the hardest testing problems in distributed systems engineering. The guarantees are subtle, the failure modes are nondeterministic, and most bugs only surface under specific timing or load conditions. This guide covers how to write deterministic tests for delivery
Testing
Testing observability in microservices is the discipline that nobody implements until an incident exposes the gap. You're in the middle of a production outage, you open Grafana, and the dashboard is empty. Or the traces are there but missing the tenant_id attribute that would tell you which
Testing
A game day exercise is a structured event where an engineering team deliberately causes failures in their own systems — and then observes, learns, and improves from the experience. It's the practice that turned Netflix into the company that wrote Chaos Monkey, that AWS uses before every major infrastructure
Testing
Business continuity testing is not just for disaster recovery — it's about systematically validating that your organization can maintain critical operations under a wide range of adverse conditions. Unlike DR testing (which focuses on technical recovery), BCT covers the full picture: technology, people, processes, and communication. This checklist is
Testing
Your database going down is not a matter of if — it's when. A disk fills up, a cloud provider has an AZ outage, a runaway query corrupts an index, a network partition isolates the primary. The question is whether your failover mechanism works correctly and whether your application
Testing
Kubernetes is designed for resilience — but resilience requires testing. A pod that doesn't restart cleanly, a deployment that fails to roll over to healthy nodes, a service that routes traffic to terminating pods — these failures are silent until they happen at 3am during peak traffic. This guide covers
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 failover
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. This
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 nuance,
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 evaluation strategy. This guide
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