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A Comprehensive Guide to RAG Evaluation and its Significance in AI Regulation Retrieval-Augmented Generation (RAG) has emerged as a prominent method for enhancing large language models (LLMs) by providing them with contextually relevant data to generate more accurate and tailored outputs. This approach is particularly valuable in applications such as chatbots and AI agents, where […]

Leveraging LLM-as-a-Judge: A Guide to Automated, Scalable Evaluation for Responsible AI The concept of using Large Language Models (LLMs) as judges for evaluating AI-generated responses is gaining traction. This method, often referred to as ‘LLM-as-a-Judge,’ allows for efficient, automated assessments based on specific criteria, making it an appealing alternative to traditional human evaluators who can […]

Comprehensive Analysis of LLM Benchmarks: Importance in AI Regulation and Evaluation In the rapidly evolving landscape of large language models (LLMs), the establishment of standardized evaluation frameworks is becoming crucial. With new models such as Anthropic’s Claude-3 Opus, Google’s Gemini Ultra, and Mistral’s Le Large emerging frequently, the necessity to systematically quantify and compare LLM […]

**Introduction** In the expanding landscape of Artificial Intelligence, Large Language Models (LLMs) are rapidly evolving to handle complex, real-life tasks with minimal human oversight. From managing grocery orders to administering financial portfolios, LLMs are increasingly autonomous. However, this autonomy brings inherent risks, as the technology becomes a target for exploitation by malicious entities. Ensuring LLM […]

As the European Union’s Artificial Intelligence Act takes effect, businesses and regulators alike face a new era of AI oversight. The EU AI Act is a regulatory framework that classifies AI systems based on risk and imposes specific requirements for compliance, especially for high-risk AI applications. Within this framework, the importance of benchmarking AI systems […]