Skypoint’s Post

Retrieval Augmented Generation: What It Is and Why It Matters for Enterprise AI Retrieval Augmented Generation (RAG) has become essential for IT leaders and enterprises looking to implement generative AI. By using a large language model (LLM) and RAG, enterprises can ground an LLM in enterprise data, improving the accuracy of outputs. #rag #llm #genai

Interesting article from Deloitte Insights Insights about the next steps in AI evolution and the human element. The three main pillars are Small Language Models (SLMs) for efficiency, Multimodal Models for versatile data processing, and Agentic AI for autonomous task execution. Human expertise remains crucial for ethical decision-making, accountability, and adaptability, ensuring AI aligns with societal values and operates effectively. #AI #ArtificialIntelligence #TechTrends #Innovation #DigitalTransformation #FutureOfWork #AIethics #MachineLearning #DataScience #AIagents

Retrieval Augmented Generation: What It Is and Why It Matters for Enterprise AI Retrieval Augmented Generation (RAG) has become essential for IT leaders and enterprises looking to implement generative AI. By using a large language model (LLM) and RAG, enterprises can ground an LLM in enterprise data, improving the accuracy of outputs. #rag #llm #genai

To overcome AI grounding challenges, we must dig deeper #AI #AIio #BigData #ML #NLU #Futureofwork http://ow.ly/3g3I30sH01b

To overcome AI grounding challenges, we must dig deeper #AI #AIio #BigData #ML #NLU #Futureofwork http://ow.ly/3g3I30sH01b

It’s tough to bring generative AI projects out of POC and into production. We interviewed hundreds of tech leaders, and only 17% said their applications were accurate enough to scale. The main issue? Large language models are powerful but often miss the specialized knowledge that businesses need to go live. More companies are turning to graph-based RAG to overcome these hurdles and make AI truly production-ready. 🔗 https://lnkd.in/gp-CcDXR #GenerativeAI #EnterpriseAI #AIPOC

For many years, there has been speculation about the potential impacts of AI on enterprises. Now we’re seeing companies from diverse sectors starting to leverage large language models and generative AI. #GenerativeAI #LLMs #LargeLanguageModels by Naren Narendran thanks to Aerospike

To overcome AI grounding challenges, we must dig deeper #AI #AIio #BigData #ML #NLU #Futureofwork http://ow.ly/3g3I30sH01b

Unlock the potential of digital transformation. Seth Earley dives deep into the challenges and opportunities of corporate data management in the era of generative AI and large language models (LLMs) in his latest CustomerThink article. Seth emphasizes the critical role of efficient data retrieval in leveraging AI for business success. If you're navigating the complexities of digital transformation, this is a must-read for practical insights on making your data work smarter for you. https://hubs.ly/Q02Pyyph0 #LLMs #DigitalTransformation #GenerativeAI

Happy to share my thoughts on the issues with unstructured data in delivering the quest of more output with AI. Thanks AI Magazine for sharing my thoughts! Key points I talked about - ⚫ Data management capacity requirements to scale with AI is exponentially larger which should be taken into account by enterprises ⚫ There should be a balancing act between quality and quantity of data. Without this balance, there is no AI! ⚫ Responsible data management practices implemented at the human or organizational level will become increasingly important as AI advances. Read the interview here - https://lnkd.in/epPhZ-ZC #ResponsibleAI #datamanagement #dataquality