Practical content on AI testing, automation engineering, QA strategy, and quality engineering from the VeraxaAI team.
LLM applications break in ways that traditional test automation cannot catch. This guide covers the evaluation frameworks, test dataset design, and tooling you need to validate language model behavior systematically.
Most Playwright tutorials show you how to write your first test. This post covers what makes test suites maintainable at scale: page object models, fixture strategy, parallel execution, and CI integration patterns.
Agentic AI systems are fundamentally harder to test than single-turn LLMs. This guide covers tool-call accuracy testing, state management validation, and how to build regression coverage for agent workflows.
Prompt injection is one of the most underestimated security risks for AI systems. Learn the attack categories, how to build a test library, and how to implement defenses that actually hold up.
Most startups either skip QA entirely or over-invest in automation before they have stable features. This post gives you a practical QA sequencing strategy for the first 12 months of product development.
A structured pre-launch validation checklist for chatbots and conversational AI — covering accuracy, safety, persona consistency, edge cases, and the adversarial inputs your users will eventually try.
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