Noureddine Ouboullah’s Post

Generative AI is quietly redefining the meaning of “data-driven”, not by adding new dashboards, but by removing the distance between humans and data itself. Traditional analytics depended on structured queries, technical expertise, and long lead times. Generative AI collapses that process. It can explore heterogeneous datasets, synthesize context across departments, and generate predictive insights in natural language. What once required SQL fluency or data science training now happens through a conversation: “Show me the leading indicators of customer churn” becomes an instant dialogue, not a ticket in a queue. This democratizes insight creation, letting every role, from finance to operations, become a data participant. Beyond accessibility, the value is multiplicative: Faster decisions, richer pattern recognition, and adaptive models that scale with the business. Generative AI doesn’t replace data analysts, it expands them into every employee. The future of analytics is not about querying data; it’s about conversing with it. #AI #DataAnalytics #GenerativeAI #LLM #BusinessIntelligence #DataDemocratization #PredictiveAnalytics #DigitalTransformation More information: 🔗https://lnkd.in/eYehV99m

RAG systems that achieve 65-75% accuracy are impressive, right? Good enough for Production? not so sure... It’s the accuracy "dead end" where most companies get stuck. Contextual AI's agents achieve 90%+ accuracy, as their advanced RAG system is built on a unified platform underpinned by Elastic, making their agents ready for high-value and complex production use cases. So, what was the magic? ✅ Hybrid Search (Keyword + Vector): This is the key. They use Elastic to run keyword and vector searches simultaneously. This finds what's actually relevant, not just what's semantically similar. And the best part? This isn't a toy demo. Contextual AI's platform operates across millions of complex, unstructured documents, managing repositories with 22 million chunks. Ready to move your RAG from Prototype to Production? Just send a DM! #Elastic #GenAI #RAG #AI #Search #VectorSearch #ProductionAI

If you blink in AI, you miss a breakthrough. One day it’s RAG. Next week, it’s Agentic AI. Then Large Reasoning Models quietly redefine what we mean by “intelligence.” Every time I dive into something new, I find ten more things that didn’t exist yesterday.  And that’s the wild part - this isn’t hype anymore. It’s the new baseline. So I tried mapping out what’s actually shaping AI today - and ended up with seven shifts that feel less like trends, and more like forces of nature. 🔽 🤖 Agentic AI The shift: AI doesn't wait for instructions anymore-it pursues goals. Say “analyze user churn,” and it doesn’t give you a report. It queries databases, spots patterns, generates insights - autonomously. 🧠 Large Reasoning Models (LRMs) The shift: AI that shows its work. Models like OpenAI’s o1 can reason through problems, not just recall data. It’s the leap from memorization -> problem-solving. 📚 Vector Databases The shift: Semantic memory at scale. They store knowledge in multi-dimensional space, letting AI remember meaning - not just text. This is why RAG systems can recall facts that static LLMs forget. 🔗 RAG (Retrieval-Augmented Generation) The shift: From hallucination to verification. Instead of guessing, AI now checks its sources. The result: fewer hallucinations, more truth. The difference between a storyteller and a researcher. 🔌 MCP (Model Context Protocol) The shift: Universal AI interoperability. Think of MCP as a USB-C for intelligence - one protocol to connect AI models with any app, database, or API. 🎯 Mixture of Experts (MoE) The shift: Specialist networks on demand. Different tasks, different brains. Models like Mixtral activate only the experts needed - efficiency meets depth. ⚡ASI (Artificial Superintelligence) We’re combining autonomy, reasoning, memory, verification, and specialization - at once. The architecture of superintelligence isn’t hypothetical anymore. The real question: Are we ready for what we’re building? AI isn’t just learning from us anymore. It’s learning with us - and maybe, soon, ahead of us. 💭 And just like that, while you’re reading this, AI has already moved on.

Retrieval-Augmented Generation, or RAG, is an AI approach that really impressed me because it lets models provide answers not just from their existing training but by pulling in fresh, relevant information in real-time. This way, AI responses become smarter, more accurate, and stay up to date with the latest data. What is RAG? In simple terms, RAG combines two important things: -: It looks up relevant information from external sources like databases, documents, or the internet. -: It then helps a language model create smarter, more informed responses by using that info. Instead of just relying on what the AI remembered from its training, RAG lets it pull in fresh, real-time knowledge so it can answer questions better or solve problems more effectively. How Does RAG Work? Here is how I understand it: You ask a question or send a prompt. The system searches through a large collection of documents or databases to find the most relevant info. It combines that info with your original question to form a better prompt. The AI model generates a response based on this enhanced input. The answer you get is more accurate and grounded in real facts. RAG vs. AI Agents — What’s the Difference? From my research, I found that RAG focuses on making one-shot answers more accurate by bringing in external facts. On the other hand, AI agents are more interactive and autonomous — they can make decisions, handle multiple steps in a task, and adapt to changing conditions by using various tools or APIs. So, RAG is great for giving reliable, fact-based answers, while AI agents act more like smart assistants that plan and carry out complex workflows. Where can we See RAG in Action? Some practical uses I came across include: Chatbots giving detailed and updated customer support. Enterprise systems helping employees find exact info quickly from tons of documents. Why is RAG Important? Because it lets AI tap into up-to-date information, RAG helps cut down errors and makes AI much more trustworthy. This is very useful for businesses that need reliable and timely AI support. Challenges and What’s Next? One challenge with RAG is making sure it picks the right data during retrieval. Also, while RAG doesn’t handle autonomous decision-making, there is a newer concept called “Agentic RAG” that combines the accuracy of RAG with the decision-making power of AI agents — this is something exciting to watch out for. #AI #MachineLearning #RAG #ArtificialIntelligence #machinelearning

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

I've recently been asked how I imagine the development of user–system interaction given the ongoing advancement of AI. I remember when there was no AI or Machine Learning (ML). We built systems where the user performed an action in one step, and in the following step, we simply assumed the previous action to be correct and reliable. Research in Machine Learning led to the creation of test-case databases coupled with corresponding performance measurements. In many cases, human output served as the gold standard -- the unbeatable benchmark for any method. The introduction of ML into commercial systems brought a new set of challenges: error modes that were atypical for humans tended to undermine trust in these promising yet black-box-like systems. An early mitigation strategy was to compute statistics on human performance and use them as a baseline to showcase and prove the performance of the new systems. Still, error cases continued to attract negative spot-light. This led to a new approach: center everything around the human user, with AI as just one of many tools in a dashboard. 👉 Most AI-based workflows now include checkpoints or mechanisms for user intervention, emphasizing our awareness of possible AI mistakes. “Don’t worry about errors” so to speak -- the human operator drives, checks, and corrects. So, what’s next? As usage grows and error rates decrease, trust increases. Following the AI’s suggestions becomes the default. The interaction shifts -- the AI drives and prompts; the user supports and observes. At some point, it works so well that we begin to ask ourselves whether we still need to overload the user with all the details and clutter the interface. We’ll be back to the clean, one-line prompt interfaces we’ve come to appreciate so much. Photo background: https://lnkd.in/dvnvdASr

𝗪𝗵𝘆 𝗗𝗼 𝗪𝗲 𝗡𝗲𝗲𝗱 𝗦𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲𝗱 𝗢𝘂𝘁𝗽𝘂𝘁? When we ask an AI model a question, it usually replies in plain text — like paragraphs or bullet points. That’s great for humans to read, but not for machines to understand or use directly. This is where 𝗦𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲𝗱 𝗢𝘂𝘁𝗽𝘂𝘁 comes in. It means the AI gives its answer in a specific, organized format — 𝗹𝗶𝗸𝗲 𝗮 𝘁𝗮𝗯𝗹𝗲, 𝗹𝗶𝘀𝘁, 𝗼𝗿 𝗝𝗦𝗢𝗡 — instead of random text.  𝗘𝘅𝗮𝗺𝗽𝗹𝗲: ❌ 𝘜𝘯𝘴𝘵𝘳𝘶𝘤𝘵𝘶𝘳𝘦𝘥 𝘰𝘶𝘵𝘱𝘶𝘵: Yellow.ai is a conversational AI startup founded in 2016. Mad Street Den is a computer vision company founded in 2013. ✅ 𝘚𝘵𝘳𝘶𝘤𝘵𝘶𝘳𝘦𝘥 𝘰𝘶𝘵𝘱𝘶𝘵: [  {"name": "Yellow.ai", "industry": "Conversational AI", "founded": 2016},     {"name": "Mad Street Den", "industry": "Computer Vision", "founded": 2013} ] Now this data can be easily used in apps, dashboards, or reports — without any manual cleanup.  𝗪𝗵𝘆 𝗜𝘁 𝗠𝗮𝘁𝘁𝗲𝗿𝘀: It makes AI results 𝗺𝗮𝗰𝗵𝗶𝗻𝗲-𝗿𝗲𝗮𝗱𝗮𝗯𝗹𝗲 and easy to integrate with tools. It 𝗿𝗲𝗱𝘂𝗰𝗲𝘀 𝗵𝘂𝗺𝗮𝗻 𝗲𝗳𝗳𝗼𝗿𝘁 in cleaning or organizing data. It ensures consistency and accuracy across systems. 𝗜𝗻 𝘀𝗶𝗺𝗽𝗹𝗲 𝘁𝗲𝗿𝗺𝘀: 𝘚𝘵𝘳𝘶𝘤𝘵𝘶𝘳𝘦𝘥 𝘰𝘶𝘵𝘱𝘶𝘵 𝘩𝘦𝘭𝘱𝘴 𝘈𝘐 𝘮𝘰𝘷𝘦 𝘧𝘳𝘰𝘮 𝘨𝘪𝘷𝘪𝘯𝘨 𝘢𝘯𝘴𝘸𝘦𝘳𝘴 𝘵𝘰 𝘨𝘪𝘷𝘪𝘯𝘨 𝘢𝘤𝘵𝘪𝘰𝘯𝘢𝘣𝘭𝘦 𝘥𝘢𝘵𝘢. 𝘐𝘵 𝘣𝘳𝘪𝘥𝘨𝘦𝘴 𝘵𝘩𝘦 𝘨𝘢𝘱 𝘣𝘦𝘵𝘸𝘦𝘦𝘯 𝘩𝘶𝘮𝘢𝘯 𝘭𝘢𝘯𝘨𝘶𝘢𝘨𝘦 𝘢𝘯𝘥 𝘮𝘢𝘤𝘩𝘪𝘯𝘦 𝘶𝘯𝘥𝘦𝘳𝘴𝘵𝘢𝘯𝘥𝘪𝘯𝘨. 

Enterprises spent billions on AI tools. Most sit unused. ThoughtSpot's different approach doubled user adoption while competitors struggle with basic engagement. The numbers tell a clear story. ThoughtSpot just reported 133% year-over-year growth in platform usage. Over 52% of customers actively use their AI agent, Spotter. Most AI analytics tools gather dust after deployment. What's different here? 🔍 Natural language queries that actually work 🤖 AI agents built for analytics, not bolted on later 📊 Self-service that business users embrace 💡 Trusted insights that drive decisions Act-On saw 60% more report usage in just 30 days. They recouped implementation costs through revenue gains in a month. McKinsey reports 78% of organizations use AI in at least one business function in 2025. Up from 72% in early 2024. But usage rates remain low across most platforms. The lesson? AI isn't just about technology. It's about adoption. Building tools people actually want to use. Making complex analytics feel simple. Delivering value from day one. ThoughtSpot now serves 40% of Fortune 25 companies. Gartner named them a Leader in Analytics and BI Platforms for 2025. The AI analytics market is heating up. Which companies in your industry are seeing real adoption vs just AI theater? #AIAnalytics #BusinessIntelligence #EnterpriseAI Tadoju V V L Samyuktha 𝗦𝗼𝘂𝗿𝗰𝗲꞉ https://lnkd.in/gGDRn_kq

Master Prompt Engineering: A 6-Step Guide to Getting Sharper AI Responses https://lnkd.in/dfWeH7G6 Still getting generic, unusable results from your AI prompts? 😫 It's not the AI's fault, it's your instructions! The latest article from AI Tools Guide unveils the "Secret to Sharper AI Prompts," highlighting that prompt engineering, not coding, is rapidly becoming the new superpower. The problem isn't the AI's capability, but our tendency to treat it like a search engine instead of a powerful engine that needs precise fuel. This game-changing guide introduces the powerful TCGREI Framework – a 6-step method to transform your AI interactions: Task: Define the AI's role, action, and desired format. Context: Provide crucial background (audience, tone, situation). Goal: Clarify the specific outcome and purpose. References: Show examples of preferred style or structure. Evaluate: Critically assess the AI's first draft. Iterate: Refine and polish through follow-up instructions. This systematic approach moves you from casual queries to structured commands, addressing common pitfalls like the "lost in the middle" problem with strategic prompt structuring. It's time to stop settling for average and start unlocking truly insightful, well-crafted results that work for you. ✅ Elevate your AI game and become one of the top 1% of users getting consistently sharp outputs! 🚀 #AIPrompts #PromptEngineering #ArtificialIntelligence #AICoaching #DigitalTransformation

🚀 𝐑𝐀𝐆 𝐄𝐱𝐩𝐥𝐚𝐢𝐧𝐞𝐝: 𝐓𝐡𝐞 𝐄𝐧𝐭𝐫𝐲-𝐋𝐞𝐯𝐞𝐥 𝐓𝐫𝐚𝐢𝐧𝐞𝐞 (𝐄𝐋𝐓) 𝐀𝐧𝐚𝐥𝐨𝐠𝐲 𝐟𝐨𝐫 𝐀𝐈 If you've spent any time working with AI, you know that sometimes those big, brilliant language models can be a little too general. They need an injection of current, real-world context to be truly useful. That's where 𝑹.𝑨.𝑮. (𝑹𝒆𝒕𝒓𝒊𝒆𝒗𝒂𝒍-𝑨𝒖𝒈𝒎𝒆𝒏𝒕𝒆𝒅 𝑮𝒆𝒏𝒆𝒓𝒂𝒕𝒊𝒐𝒏) comes in—it's the smart solution making modern AI relevant, reliable, and grounded in the facts you need. Let's break down this powerful technique using a simple, relatable analogy: 𝑻𝒉𝒆 𝑬𝒏𝒕𝒓𝒚-𝑳𝒆𝒗𝒆𝒍 𝑻𝒓𝒂𝒊𝒏𝒆𝒆 (𝑬𝑳𝑻). 1️⃣ 𝐑𝐞𝐭𝐫𝐢𝐞𝐯𝐚𝐥: 𝐅𝐞𝐭𝐜𝐡𝐢𝐧𝐠 𝐈𝐧𝐟𝐨𝐫𝐦𝐚𝐭𝐢𝐨𝐧 🔍 -The ELT (our initial model, trained on general knowledge) joins a company. - The ELT needs context on the work culture and latest technology. They will actively look for internal documentation and stay updated rather than being stagnant. 2️⃣ 𝐀𝐮𝐠𝐦𝐞𝐧𝐭𝐚𝐭𝐢𝐨𝐧: 𝐀𝐝𝐝𝐢𝐧𝐠 𝐂𝐨𝐧𝐭𝐞𝐱𝐭 & 𝐄𝐧𝐡𝐚𝐧𝐜𝐢𝐧𝐠 𝐊𝐧𝐨𝐰𝐥𝐞𝐝𝐠𝐞 ✨ - In today's age, Augment means to understand the context and use the additional information to enhance the knowledge one already has. - The ELT obtains the data and needs to 𝒖𝒏𝒅𝒆𝒓𝒔𝒕𝒂𝒏𝒅 𝒕𝒉𝒆 𝒄𝒐𝒏𝒕𝒆𝒙𝒕 and how it can be applied in their daily job. Just having the data is not important; putting it into use is equally important. 3️⃣ 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐨𝐧: 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐧𝐠 𝐒𝐞𝐧𝐬𝐢𝐛𝐥𝐞 𝐎𝐮𝐭𝐩𝐮𝐭𝐬 💡 - Once the data is fetched and context is understood, it's time to 𝒑𝒖𝒕 𝒕𝒉𝒆 𝒌𝒏𝒐𝒘𝒍𝒆𝒅𝒈𝒆 to use by applying it to their daily job and enhancing the current workflow with these gained ideas. This is Generation. 𝐓𝐡𝐞 𝐑𝐀𝐆 𝐌𝐨𝐝𝐞𝐥 𝐢𝐧 𝐀𝐜𝐭𝐢𝐨𝐧: Similarly, the RAG LLM model 𝒓𝒆𝒕𝒓𝒊𝒆𝒗𝒆𝒔 information from the most updated database, 𝒖𝒏𝒅𝒆𝒓𝒔𝒕𝒂𝒏𝒅𝒔 𝒕𝒉𝒆 𝒄𝒐𝒏𝒕𝒆𝒙𝒕 (augmentation), and 𝒕𝒉𝒆𝒏 𝒂𝒑𝒑𝒍𝒊𝒆𝒔 𝒊𝒕 𝒊𝒏 𝒈𝒆𝒏𝒆𝒓𝒂𝒕𝒊𝒏𝒈 𝒔𝒆𝒏𝒔𝒊𝒃𝒍𝒆 𝒐𝒖𝒕𝒑𝒖𝒕𝒔 based on the prompt. Hope this helps simplify a core component of modern AI architecture! Tagging great AI content creators Prabh Nair and Chidambaram Narayanan for spreading the word. #RAG #LLM #ArtificialIntelligence #GenerativeAI #TechExplained