When building AI applications with LLMs, many teams are still "vibe testing" their way to production. This guide shows how file-based prompts and comprehensive test scenarios build reliable review analysis systems that handle real-world complexity, allowing you to build a solid set of repeatable automated regression tests.

LLMs produce non-deterministic outputs, making traditional exact-match testing ineffective. How can you verify an application response is contextually accurate when the response can vary with every request? Let's take a look at promptfoo!

A look at the recently published Aspire roadmap for 2025, focusing on its code-first DevOps evolution, polyglot aspirations, and AI orchestration, and more.

Discover how GitHub Copilot Agents can function as AI developers on your team, automating development while complementing human developers' strategic skills.

Web chat portals can profoundly empower your team to build amazing things. Let's see how these systems can work securely and offer unique value to your team.

Artificial intelligence has moved from a futuristic concept to an everyday business tool. With these new tools data is your most valuable asset. Are you protecting it? Let’s explore how Leading EDJE can help to ensure you are not at risk.

AI systems are powerful, but hard to constrain, measure, and control. Let's see how Microsoft.Extensions.AI.Evaluation helps with AI systems development.

A recap of the most exciting announcements from Microsoft Build 2025 according to your friendly neighborhood developer.

Kernel Memory gives us a simple and flexible way of indexing and searching documents and websites using our own models and vector storage solutions using .NET.

Model Context Protocol (MCP) and Agent2Agent (A2A) are rising stars in the world of AI. Let's see what they are and how they can work together.