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Will context define the next era of intelligent systems? AI platforms are only as accurate as the data they’re given. But context engineering could help platforms sort data and deliver at scale. Read more: https://lnkd.in/enAVbTma Partner Content by Moody's #AI #bankingtransformation #innovation Moody's Analytics

𝐇𝐮𝐦𝐚𝐧 𝐢𝐧 𝐭𝐡𝐞 𝐋𝐨𝐨𝐩, 𝐇𝐮𝐦𝐚𝐧 𝐅𝐢𝐫𝐬𝐭 𝐢𝐧 𝐊𝐧𝐨𝐰𝐥𝐞𝐝𝐠𝐞 𝐑𝐞𝐩𝐫𝐞𝐬𝐞𝐧𝐭𝐚𝐭𝐢𝐨𝐧 “𝑇𝑒𝑐ℎ𝑛𝑜𝑙𝑜𝑔𝑦 𝑎𝑙𝑜𝑛𝑒 𝑛𝑒𝑣𝑒𝑟 𝑠𝑜𝑙𝑣𝑒𝑠 𝑎𝑛 𝑒𝑛𝑡𝑒𝑟𝑝𝑟𝑖𝑠𝑒’𝑠 𝑑𝑎𝑡𝑎 𝑞𝑢𝑎𝑙𝑖𝑡𝑦 𝑝𝑟𝑜𝑏𝑙𝑒𝑚𝑠.” — 𝐺𝑟𝑎𝑝ℎ𝑅𝐴𝐺.𝑖𝑛𝑓𝑜, “𝐺𝑟𝑎𝑝ℎ𝑅𝐴𝐺 𝐶𝑜𝑚𝑝𝑎𝑟𝑒 𝑎𝑛𝑑 𝐶𝑜𝑛𝑡𝑟𝑎𝑠𝑡: 𝐴𝑊𝑆 𝐿𝑒𝑡𝑡𝑟𝑖𝑎 𝑣𝑒𝑟𝑠𝑢𝑠 𝑡ℎ𝑒 𝐸𝑌 𝐺𝑟𝑎𝑝ℎ𝑤𝑖𝑠𝑒 𝐴𝑝𝑝𝑟𝑜𝑎𝑐ℎ” see: https://lnkd.in/eJeMu5TK The thought for this post began after reading LiveMint’s piece on OpenAI’s latest breakthrough & Sobering reality check: https://lnkd.in/eHUKSmeZ It’s a timely reminder that while AI models continue to get bigger, what truly defines intelligence isn’t scale — it’s 𝗴𝗿𝗼𝘂𝗻𝗱𝗶𝗻𝗴. Grounding in curated, contextual, human-validated knowledge. At the Knowledge Summit Dublin 2025, 𝐆𝐫𝐚𝐩𝐡𝐰𝐢𝐬𝐞 presented “Connecting the Dots: Building a Collaborative Knowledge Hub with LLMs and Graphs” (see video: https://lnkd.in/e_9X_gBa) Their message resonated deeply: LLMs are only as good as the 𝐤𝐧𝐨𝐰𝐥𝐞𝐝𝐠𝐞 𝐬𝐜𝐚𝐟𝐟𝐨𝐥𝐝𝐢𝐧𝐠 we provide — and that scaffolding must be human-first. In today’s GraphRAG systems, annotated data and well-structured knowledge graphs form the 𝐬𝐞𝐦𝐚𝐧𝐭𝐢𝐜 𝐛𝐚𝐜𝐤𝐛𝐨𝐧𝐞 that keeps generative AI aligned with enterprise reality. > Without them, the model hallucinates patterns where no knowledge exists. > With them, reasoning becomes traceable, interpretable, and verifiable. Human-first knowledge representation means encoding not just what connects, but why it was connected — provenance, context, intent. SHACL validation, annotation workflows, and collaborative curation are the new pillars of trustworthy LLMs. Let’s remind ourselves: - Ontologies evolve with human consensus, not automation. - Data quality emerges from stewardship, not scale. - True enterprise intelligence requires humans in the loop — to guide, correct, and contextualize AI reasoning. ⸻ 𝐈𝐧 𝐬𝐡𝐨𝐫𝐭: 𝐀𝐧𝐧𝐨𝐭𝐚𝐭𝐞𝐝 𝐝𝐚𝐭𝐚 + 𝐊𝐧𝐨𝐰𝐥𝐞𝐝𝐠𝐞 𝐆𝐫𝐚𝐩𝐡𝐬 + 𝐇𝐮𝐦𝐚𝐧 𝐕𝐚𝐥𝐢𝐝𝐚𝐭𝐢𝐨𝐧 = 𝐑𝐞𝐬𝐩𝐨𝐧𝐬𝐢𝐛𝐥𝐞 𝐋𝐋𝐌 𝐆𝐫𝐨𝐮𝐧𝐝𝐢𝐧𝐠

New inspiring AI insights from our colleague, Branislav Popović, AI & ML Expert and Principal Research Fellow! Learn how the model context protocol enhances AI’s strategic agility through context-aware orchestration, and why choosing the right client, balancing performance trade-offs, and ensuring strong governance are essential for effectively deploying adaptive, intelligent AI systems. Find out more here: https://lnkd.in/dtwmJNjw

Quantexa Makes Its Decision Intelligence Platform ‘Agent Ready’ to Solve the Hardest Problems in AI: Data Fragmentation & Context Read more: https://lnkd.in/dm3fsqaM #IndiaTechnologyNews #Quantexa #DecisionIntelligence #AIInnovation #AgentReady #DataFragmentation #ContextualAI #EnterpriseAI #DataAnalytics #ArtificialIntelligence

Of course. Here is a LinkedIn-style summary of the article: 🚀 **The AI Context Crisis: Are Your Models Flying Blind?** The AI landscape is exploding with new models and tools, but a critical bottleneck is emerging: the **lack of context**. As developers rush to build smarter applications, they're hitting a wall. Modern AI models, for all their power, often operate with a shallow understanding of the user's specific situation, history, or environment. This "context crisis" means your AI assistant might not remember your last request, or a business AI might make a recommendation without understanding the full scope of a project. This isn't just a minor inconvenience—it's the fundamental barrier between a neat demo and a truly intelligent, reliable system that can be trusted with complex tasks. The next major leap in AI won't be about having more parameters, but about giving models a richer, more persistent memory and a deeper situational awareness. The race is no longer for the biggest model, but for the smartest one. The focus is shifting to solving the context problem. Who will build the AI that truly *understands*? #AI #MachineLearning #SoftwareDevelopment #ContextAware #FutureOfAI #TechInnovation

How can organisations improve the reliability of their AI platforms? Why context engineering could help supply platforms with the right information in the right way, delivering better results. Read more: https://lnkd.in/enAVbTma Partner Content by Moody's #AI #bankingtransformation #innovation Moody's Analytics

🤖 GenAI vs AI Agents vs Agentic AI vs ML vs Data Science vs LLM — What’s the Difference? AI isn’t one thing — it’s an ecosystem. Each layer plays a unique role in building today’s intelligent systems. Let’s break it down 👇 1️⃣ Generative AI Creates new content — text, images, videos, or code. Powered by diffusion models, GANs, and transformers. 💡 Think ChatGPT, Midjourney, or Runway. 2️⃣ AI Agents Autonomous systems that reason, act, and adapt. Use APIs, tools, and feedback loops to achieve goals. 3️⃣ Agentic AI The evolution of agents — self-improving, reasoning, and planning systems. Focuses on autonomy, self-reflection, and human alignment. 4️⃣ Machine Learning (ML) Finds patterns and makes predictions from data. Includes supervised, unsupervised, and reinforcement learning. 5️⃣ Data Science The data backbone — collecting, analyzing, and visualizing insights. Combines statistics, experimentation, and data ethics. 6️⃣ Large Language Models (LLMs) Massive transformer-based models trained on text data. Understand and generate natural language at scale. ✅ In short: Data Science → Builds the foundation ML → Learns from data LLMs + GenAI → Create content AI Agents + Agentic AI → Take intelligent action Together, they form the complete AI intelligence stack driving the future. ⚙️💡 Credit: Shalini Goyal Follow Buzz Data Science for fresh insights, trends, and hands-on learning. #AI #GenAI #DataScience #MachineLearning #LLMs #AICommunity #AgenticAI

”Enterprises have begun to discover what the generative AI hype can obscure: large language models are convincing but inconsistent unless fed the right data. Markets move on data and analysis; a misplaced figure, a stale disclosure, or a hallucinated data point can make the difference between sound judgment and costly error. That’s why the true differentiator in enterprise-grade generative AI isn’t style, but substance – specifically, context engineering: the structuring, selection and delivery of the right data into an AI system’s context window at the right moment. Without it, models are more likely to hallucinate, miss critical signals or provide generic answers unfit for high-stakes decision-making.” Click on the link below, read it all. #ContextEngineering #PromptEngineering #EnterpriseAI #LLM #GenerativeAI

2026 Belongs to AI Meaning Builders, Not Model Builders - For the better part of a decade, enterprises have been racing to build bigger models and gather more data, believing scale alone would unlock artificial intelligence at full capacity. Yet despite remarkable breakthroughs in generative AI, most organizations still find themselves stuck at the same frustrating juncture: the last mile between technical capabilities and accurate outputs that agentic systems can be built off of. Models horsepower can be 10X but if it can’t perform at high accuracy, it’s doomed to a life of shelfware. The reason is no longer a mystery. The bottleneck to enterprise AI isn’t data or compute […] - https://lnkd.in/evhncAcU

2026 Belongs to AI Meaning Builders, Not Model Builders - For the better part of a decade, enterprises have been racing to build bigger models and gather more data, believing scale alone would unlock artificial intelligence at full capacity. Yet despite remarkable breakthroughs in generative AI, most organizations still find themselves stuck at the same frustrating juncture: the last mile between technical capabilities and accurate outputs that agentic systems can be built off of. Models horsepower can be 10X but if it can’t perform at high accuracy, it’s doomed to a life of shelfware. The reason is no longer a mystery. The bottleneck to enterprise AI isn’t data or compute […] - https://lnkd.in/evhncAcU