Hot AI take #1: The next great move in AI will not be an improvement in the power of the LLM itself. There is already evidence that “bigger is better” is meeting its limitations — smaller models are being trained which are matching the capabilities of big models. Properly created ML anomaly detection pipelines are a great example of that. The next great move is properly wrapping the LLM in ways that treat it like a child. ReAct and similar wrappers which give an LLM a limited set of options are going to provide a great tool set in the near future. If you feel the urge to think of LLMs as thinking, then view them like the 6 year old you left unattended in a kitchen and being surprised they can’t cook. #AI #MachineLearning #Innovation
The future of AI: Wrapping LLMs like a child
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Now you know which scores represent "Quality" in your AI agent. But next: Applying it to every AI call in production? That’s the real game. You can’t check thousands of user interactions manually. You need automation. 3 fundamental ways: 1. Human Annotation → Ground truth. Small sample, deep accuracy. 2. Rule-based Checks → Black-and-white. Fast. Cheap. Every call. 3. LLM-as-a-Judge → Scales nuance (e.g. helpfulness, relevance). Combine all 3 → Continuous, reliable, scalable evals. That’s how you stop hoping your AI works… and know it does. Diving into AI Observability & Evals (5/6) #AIObservability #Tracing #LLM #AI
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AI won’t fix bad processes or messy data. It makes them worse. Harvard Business Review calls this the “AI experimentation trap”, rushing to layer AI on top of broken workflows or cluttered data. The result? Faster confusion, noisier outputs, wasted investment. If it can’t run solidly as a manual process, it has no business being automated. Clean and align data first, or AI will just scale bad inputs. Fix first. Automate second. Scale with AI last. https://lnkd.in/e2ZBhnDY #AI #DigitalTransformation #Leadership #Automation #DataStrategy #ProcessExcellence #CIO #BusinessValue
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An AI that isn’t monitored is just guessing blindly over time. Here’s why: Once a model is deployed, the world around it keeps changing. Data shifts, patterns evolve, and that’s when model drift sets in. Accuracy drops, and suddenly your AI is making worse predictions than it did on day one. That’s why monitoring is critical. Dashboards track performance, drift detectors flag issues, and alerts let you know before the system goes off course. It’s how you keep AI reliable, accurate, and trustworthy — not just at launch, but for the long run. 👉 Next, we’ll cover how to safely roll out AI updates without breaking everything. Follow so you don’t miss it. #AI #MLOps #MachineLearning #ModelMonitoring #DeepLearning
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MIT reports a staggering 95% of AI projects fail to deliver meaningful outcomes. Sam Altman of OpenAI cautions against viewing AI as a "silver bullet". Are we in an AI bubble, and what does this mean for your strategy? Let's discuss! #AI #TechBubble #Innovation #AIProjects #BusinessStrategy
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I found a cheat code for working with AI. Everyone talks about using automation and AI for efficiency, but I've learned that completely relying on it can sometimes backfire. I used to let the AI do the first pass on a document and I'd spend more time double-checking generic feedback than I would have by just doing the work myself. My process is different now. I do a thorough manual check first. Then, I give the AI my initial findings as a brief. This teaches the AI what to look for and helps it deliver specific, non-generic feedback. It's a complete game-changer. My time is used more efficiently, and the final result is far more accurate. The biggest lesson? A machine can't replace a careful eye, but it can absolutely supercharge it. #DataAccuracy #AI #Automation #DataIntegrity #Research
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AI isn’t plug-and-play ... and AI agents especially won’t work without one thing: data. In this short, I explain why training and tuning AI agents requires high-quality, well-structured data for things like training and tuning. Without the right pipelines, acquisition, and cleanliness, your AI systems will fail. Or worse, give misleading outputs. If you’re serious about integrating AI into your business, data readiness is not optional. It’s the foundation of everything: supervised learning, generative AI, and especially AI agents. #AI #Data #AIagents #ArtificialIntelligence #business
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🔍 RAG is changing how we tackle AI hallucinations Retrieval-Augmented Generation doesn't just make AI smarter—it makes it more trustworthy. Instead of relying purely on training data, RAG models: ✅ Pull real-time information from verified sources ✅ Ground responses in actual documents ✅ Reduce the "creative" answers that sound right but aren't The result? AI that admits when it doesn't know something rather than making up convincing fiction. For enterprise AI, this isn't just a nice-to-have—it's essential. When accuracy matters more than creativity, RAG is your safety net. #AI #MachineLearning #RAG #ArtificialIntelligence #TechTrends
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3 checks for building Human-First AI: 1️⃣ Diverse data → Bias starts at input 2️⃣ Edge-case testing → Who gets excluded when systems fail? 3️⃣ Continuous audits → AI needs monitoring, not one-time fixes Safer AI → Trusted AI. #HumanFirstAI #AITrust #AIEthics #InclusionInTech
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AI in financial services isn’t about waiting for the “perfect” tool. It’s about moving fast, iterating faster, and learning as you go. Our latest article shares the 3 key lessons we’ve learnt about speed, safety and scalability in AI development. Read the article here: https://lnkd.in/exsu5m4r #AIDevelopment #FinServInnovation #SignalScannerAI #FutureOfFinance
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