Berlin AI Engineering Group by m|rig

Large Language Models are increasingly used to evaluate, score, and audit the outputs of other AI systems, from code generation to customer interactions and risk assessments. But how can you actually design and maintain an LLM-as-a-Judge system that is trustworthy, scalable, and aligned with your business goals?

In this 60-minute online interactive webinar, we’ll explore the architectural patterns, governance frameworks, and operational practices that enable LLMs to act as reliable evaluators across domains.

You’ll learn:
🧩 Core Concepts of LLM-as-a-Judge
How evaluators differ from chatbots, copilots, and agents, and what makes them essential for assessing model quality and compliance.

🏗️ Design & Architecture Patterns
Key patterns for prompt evaluation, reasoning calibration, rubric-based scoring, multi-model arbitration, and continuous feedback loops.

⚙️ Tools & Infrastructure
Open-source and cloud solutions for evaluator orchestration, logging, monitoring, and performance tracking.

📏 Governance & Maintenance
Best practices for bias mitigation, rubric evolution, drift detection, and maintaining long-term consistency.

🏢 Real-World Use Cases
Examples from companies that use “AI judges” to review code, summarize documents, evaluate customer interactions, or enforce compliance.

🎯 Who should attend?

• AI/ML engineers and data scientists designing LLM evaluation systems
• Solution architects and MLOps professionals deploying LLM pipelines
• Compliance and model governance leads ensuring fairness and auditability
• Anyone curious about how “AI judges” are redefining quality assurance in AI

By the end of this session, you’ll know how to build, govern, and evolve an LLM-as-a-Judge framework, and how to apply it to your own AI evaluation workflows.

📅 Duration: 60 minutes

🔗 URL: https://events.teams.microsoft.com/event/4bb20580-cffe-4322-80d3-dfebab4062ce@d94ea0cb-fd25-43ad-bf69-8d9e42e4d175