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The next wave of advancements, including fine-tuned LLMs and multimodal AI, has enabled creative applications in content creation, coding assistance, and conversational agents. However, with this growth came concerns around misinformation, ethical AI usage, and data privacy, fueling discussions around responsibleAI deployment.
As a result, businesses can accelerate time to market while maintaining data integrity and security, and reduce the operational burden of moving data from one location to another. With Einstein Studio, a gateway to AI tools on the dataplatform, admins and data scientists can effortlessly create models with a few clicks or using code.
By following these guidelines, organizations can follow responsibleAI best practices for creating high-quality ground truth datasets for deterministic evaluation of question-answering assistants. Rahul Jani is a Data Architect with AWS Professional Service.
From internal knowledge bases for customer support to external conversational AI assistants, these applications use LLMs to provide human-like responses to natural language queries. We encourage you to adopt these best practices and start evaluating your generative AI question answering pipelines with the FMEval toolkit today.
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