Chaos Engineering
The discipline of experimenting on a system by intentionally injecting failures to uncover weaknesses and build confidence that the system can withstand turbulent real-world conditions.
Chaos engineering proactively tests resilience by introducing controlled failures: killing random servers, injecting network latency, simulating database outages, or exhausting disk space. The goal is to discover vulnerabilities before they cause real incidents, and to verify that fallback mechanisms actually work under pressure.
Netflix pioneered the practice with Chaos Monkey (randomly terminates production instances) and expanded it into the Simian Army. Modern tools like Gremlin, Litmus, and AWS Fault Injection Simulator make chaos experiments accessible to any team. Experiments should start small (one instance, one availability zone) and expand as confidence grows.
For AI systems, chaos engineering reveals critical failure modes: what happens when the LLM API is unavailable for 30 seconds, when vector database latency doubles, when the feature store returns stale data, or when a model returns malformed output. Running these experiments in controlled conditions ensures your fallback paths work correctly rather than discovering they are broken during a real incident.
Related Terms
A/B Testing
A controlled experiment comparing two or more variants to determine which performs better on a defined metric, using statistical methods to ensure reliable results.
Feature Flag
A software mechanism that enables or disables features at runtime without deploying new code, used for gradual rollouts, A/B testing, and targeting specific user segments.
MLOps
The set of practices combining machine learning, DevOps, and data engineering to reliably deploy, monitor, and maintain ML models in production.
Model Serving
The infrastructure and systems that host trained ML models and handle inference requests in production, optimizing for latency, throughput, and cost.
Semantic Search
Search that understands the meaning and intent behind a query rather than just matching keywords, typically powered by embedding-based similarity comparison.
CI/CD (Continuous Integration / Continuous Deployment)
An automated software practice where code changes are continuously integrated into a shared repository, tested, and deployed to production, reducing manual intervention and accelerating delivery cycles.