Sadra Amiri-Moghadam’s Post

Enjoyed an insightful conversation at Fortune's #brainstormtech event with Neo4j's CTO, Philip Rathle. His recent post provides an excellent overview of how GraphRAG can supercharge your #AI. Why consider transitioning to GraphRAG from a vector-only Retrieval Augmented Generation (RAG) approach? GraphRAG offers: - Higher accuracy and more comprehensive answers - Reduced development time and maintenance with knowledge graph utilization - Improved explainability, traceability, and governance, crucial for enterprise applications. This new approach (things, not strings) is looking like the next big step to help LLMs deliver far better results. #Neo4j #GraphRAG #AI #Technology #Innovation

🚀 Unleashing the Power of Knowledge Graphs in Generative AI! 🚀 "GraphRAG will subsume vector-only RAG and emerge as the default RAG architecture for most use cases." In the evolving landscape of artificial intelligence, the combination of Generative AI and Knowledge Graphs, as championed by the GraphRAG Manifesto, is set to transform how we access and utilize information. By integrating Knowledge Graphs with generative models, we can enhance the accuracy and relevance of AI-generated content, providing a richer, more connected understanding of the world. This approach not only boosts AI capabilities but also aligns with the need for more transparent, explainable AI systems. The future of AI is here, and it's interconnected! 🌐 Learn more: https://lnkd.in/dQrYN-yC #GraphRAG #LLM #knowledgegraphs #GenAI #RAG #Neo4j Arvind Ramachandran David Stevens Adrien Boulad Jessica Cannella Marco Bessi Jesús Barrasa

Vector database plays a critical role in supporting model training and optimizing generative AI prompting. A right platform that fit your needs can boost the outcome accuracy, personlise generated results, lower response latency, and utimately enhance user experiences of AI application. We share an aritcle which lists out 4 key aspects you must consider when selecting a vector database platform. #Vector #RAG #Prompting #FineTuning #ML #PromptEngineering #MLTraining #ModelTraining #AITraining https://lnkd.in/gU6JCJfm

In 2022/23 i published here two article about knowledge graph (without the need of a detailed and never stable anthology) and AI usage in their creation and inquiry. This is the logical evolution of that vision with LLM. The knowledge is not pure "probability", is topology.

Anthropic has introduced the Model Context Protocol, a new framework aimed at standardizing data integration into AI systems. This initiative seeks to address the growing need for more consistent and transparent methods of incorporating diverse datasets into machine learning models. By establishing clear guidelines for how data can be effectively utilized, the Model Context Protocol hopes to improve the efficiency, interpretability, and overall reliability of AI models across various industries. The protocol is designed to promote better alignment between data and AI models, ultimately enhancing their performance and reducing the risk of bias or errors in decision-making processes. Read more 👉https://lnkd.in/dZKYvtCZ #AI #MachineLearning #DataIntegration #TechDevelopment #LLMs #Technology #TechSolutions #SafeUse #DataAnalysis #Australia #Innovation with SHAVIK Visit www.SHAVIK.ai

Insightful blog post on integrating knowledge graphs with generative AI for enhanced accuracy and explainability in AI applications.

knowledge graphs in RAG, These systems are basically solving the "I know a lot, but I don't know what I know" problem for AI. Think of it like giving the AI a super-organized mental map. It helps the AI connect the dots between different pieces of info, which is huge for answering complex questions or understanding context. Plus, it's a game-changer for fact-checking – the AI can actually verify its own output against a structured knowledge base. This setup also helps with the hallucination issue that plagues a lot of LLMs. By grounding responses in a knowledge graph, we're reducing those wild, made-up answers. And here's a cool bit: it allows for more transparent AI. You can actually trace back how the AI came to its conclusions by looking at the graph pathways it used. Key takeaways: Improves context understanding Enables complex reasoning Reduces hallucinations Enhances fact-checking capabilities Increases transparency and explainability https://lnkd.in/eKdwfy9n

Anthropic has introduced the Model Context Protocol, a new framework aimed at standardizing data integration into AI systems. This initiative seeks to address the growing need for more consistent and transparent methods of incorporating diverse datasets into machine learning models. By establishing clear guidelines for how data can be effectively utilized, the Model Context Protocol hopes to improve the efficiency, interpretability, and overall reliability of AI models across various industries. The protocol is designed to promote better alignment between data and AI models, ultimately enhancing their performance and reducing the risk of bias or errors in decision-making processes. Read more 👉https://lnkd.in/ga5sFskV #AI #MachineLearning #DataIntegration #TechDevelopment #LLMs #Technology #TechSolutions #SafeUse #DataAnalysis #Australia #Innovation with SHAVIK Visit www.SHAVIK.ai

Nice article highlights what GraphRAG + VectorRAG can do together by adding Knowledge to GenAI.

FYI on summarization of 4 key considerations to discover the right vector database platform. #vector #ml #ai #prompting #finetuning #buyfromlo