How context engineering can improve AI accuracy

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

”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

A must-read for anyone building the next wave of intelligent systems. Great insights from InfoQ latest AI, ML & Data Engineering Trends Report 2025. The report highlights a key shift in the AI landscape. It’s no longer just about building bigger models, but about creating stronger data pipelines that connect structure, context, and meaning. We see this evolution as the foundation of creative intelligence. AI and ML models need more than visual data; they need metadata that helps them understand composition, context, and intent. With over 232 million rights-cleared images, videos, and vectors enriched with structured metadata from a global creator community, 123RF is helping businesses train AI that truly learns. Through our Content Licensing and AI Data Solutions, we provide datasets built for accuracy, scale, and ethical clarity. The future of AI is not just about generation, it’s about creation that’s meaningful, responsible, and intelligently powered by quality data. 🔗 Read the full report on InfoQ: https://lnkd.in/dRSW8rpJ #AI #MachineLearning #DataEngineering #123RFAIML #AIML #InfoQ

RAG is a more cost-effective approach to introducing new data to the LLM. It makes generative artificial intelligence (generative AI) technology more broadly accessible and usable. #RAGModel #GenerativeAI #LLMmodel #contentbased #AugumentedGenerativeAI

Understanding AI Model Fine Tuning and Why It Matters Fine tuning allows developers to take a pre trained model that already understands general language or image patterns and make it smarter in a specific area. It is one of the most important techniques in modern AI development, helping organizations reduce costs, save time, and achieve better accuracy with less data. https://lnkd.in/g48_2u-z

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

https://lnkd.in/gKQNEs7k Here are three questions I'd love your thoughts on: 1. What's one must-do step to make AI adoption sustainable-not just pilot-week glam-in our industry (IT + infrastructure + group operations)? 2) As AI carries more decision weight, how are we balancing speed with trust - governance, bias, and security? 3) If you had to pick one “next big thing” in AI we should gear up for in the next 12 months — what is it, e.g., on-device models, agentic workflows, data-centric AI, or something else? Would love to hear from our peers what they're noticing on the ground: what's accelerating, holding back, and what's your next move. 😊 🙏 #AI #GenerativeAI #EnterpriseAI #DigitalTransformation #AIGovernance #AIAdoption #ITLeadership #FutureOfWork

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

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