💡 RAG evaluation means tracking retrieval, generation & end-to-end performance – while balancing cost, latency & compliance. 👉Full guide: https://t.co/Izg6csApD0 #RAGEvaluation #LLM #AI

1/5 Next in our RAG Evaluation series: Answer Relevance & Correctness! 🎯 Key to delivering accurate, on-point responses. #RAGEvaluation #AIAccuracy https://t.co/kizvMaX5Bn

(1/6) Is your #RAG system giving you half-baked answers? Evaluating Retrieval-Augmented Generation isn't a single magic trick. It's a two-step tango, and mastering it requires splitting your focus! Let's dive in. #RAGEvaluation #LLMs #AI #Thread

1/5 Time to implement RAG evaluation! 🛠️ Let’s build evaluation pipelines for efficient, consistent assessments. #RAGEvaluation #AIPipelines

1/5 Let’s talk Hallucination Detection in RAG evaluation. 🕵️‍♀️ Identifying false or unsupported AI responses is crucial. #AIHallucination #RAGEvaluation

1/5 Let’s explore Retrieval Accuracy Evaluation for RAG systems! 🔍 This metric shows how well your system finds relevant info. #RAGEvaluation #AIMetrics https://t.co/jtEgI9RJ8P

AWS が RAG 評価と LLM 審査員機能を Amazon Bedrock に導入 | InfoWorld #AWSBedrock #RAGevaluation #LLMjudge #CustomConnectors https://t.co/DchF4bRaWa

農作物保険引受業務の効率化の先駆的取り組み: Amazon Bedrock と Amazon OpenSearch Service を使用した AI 主導型ソリューション | AWS パートナーネットワーク (APN) ブログ #SourceAllies #GenerativeAI #RAGevaluation #CropUnderwriting https://t.co/kAhhWJI0kw

RAGAS → Using Faithfulness to verify the reliability of answers, and Context Relevancy to check search accuracy. Setting the direction for system improvement with these two metrics! #RAGAS #RAGEvaluation #AIQuality #AWSAI #AWSAIDay