In this blog post, we will see how to perform a performance test on a retrieval-augmented generation (RAG) application properly, covering both speed and correctness.
Azure Databricks and Microsoft Fabric overlap, but they're built for different priorities. Databricks for data engineering, ML, open-source, and Spark workloads.
When browser automation fails, the thrown error is usually three steps removed from the actual cause. Treat the Playwright trace as the source of truth.
A practical guide to feature engineering at scale with Azure Databricks, covering distributed data processing with Spark and reliable storage with Delta Lake.
AI-generated code introduces integration failures that spec-based tests cannot catch. Regression testing grounded in real production behavior is the fix.
Free VS Code extension for Azure AI Foundry agent traces into your editor as an interactive timeline — see tool calls, token costs, and conversation replays.
LLMs can quickly generate web application code, but AI-written code may contain security vulnerabilities. This article reviews testing methods for LLM systems.
A walk-through of the new JDWP-based on-device debugging pipeline for ParparVM iOS apps and Android apps, with a step-by-step IntelliJ tutorial for each.
Finding bugs is what testing produces; understanding quality is why it exists. QA's future belongs to those who understand products, customers, and risks, not just bugs.
When optimizing Spring Boot integration tests, developers often focus on obvious metrics, but they do not always explain why an integration test suite is slow.