Artificial Inteligence Generative AI Guidelines 
AWS Machine Learning - AI
DECEMBER 11, 2024
In this post, we illustrate how EBSCOlearning partnered with AWS Generative AI Innovation Center (GenAIIC) to use the power of generative AI in revolutionizing their learning assessment process. Sonnet model in Amazon Bedrock. Sonnet in Amazon Bedrock.
CIO
NOVEMBER 4, 2024
Like many innovative companies, Camelot looked to artificial intelligence for a solution. The result is Myrddin, an AI-based cyber wizard that provides answers and guidance to IT teams undergoing CMMC assessments. To address compliance fatigue, Camelot began work on its AI wizard in 2023.
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CIO
JANUARY 30, 2025
While most provisions of the EU AI Act come into effect at the end of a two-year transition period ending in August 2026, some of them enter force as early as February 2, 2025. On this basis we chose to join the AI Pact, which gives guidelines and helps understand the rules of law.
AWS Machine Learning - AI
NOVEMBER 15, 2024
Principal wanted to use existing internal FAQs, documentation, and unstructured data and build an intelligent chatbot that could provide quick access to the right information for different roles. Now, employees at Principal can receive role-based answers in real time through a conversational chatbot interface.
CIO
OCTOBER 25, 2024
Framing the guardrails According to Ketchum, they were very deliberate about not developing restrictive policies around the use of AI. Rather, they put together AI adoption guidelines in consultation with experts and analysts from IDC and Gartner, as well as their legal and cybersecurity team. “We
AWS Machine Learning - AI
MARCH 5, 2025
Generative AI question-answering applications are pushing the boundaries of enterprise productivity. These assistants can be powered by various backend architectures including Retrieval Augmented Generation (RAG), agentic workflows, fine-tuned large language models (LLMs), or a combination of these techniques.
AWS Machine Learning - AI
NOVEMBER 20, 2024
Retrieval Augmented Generation (RAG) has become a crucial technique for improving the accuracy and relevance of AI-generated responses. The effectiveness of RAG heavily depends on the quality of context provided to the large language model (LLM), which is typically retrieved from vector stores based on user queries.
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