Lessons in speed, safety, and scalability for AI in finance

What is Prompt Engineering? It’s the art of crafting the right instructions so AI delivers the best possible results. Think of it like talking to a smart assistant: the clearer your request, the better the response. Mastering prompt engineering means unlocking AI’s full potential. 🚀 👉 Start learning today: https://lnkd.in/grJyhHTG #PromptEngineering #AI #FutureOfWork #EdTech #pennanteducation

Avoiding AI 👾 isn’t just risky—it’s like handing over our toolbox and hoping for the best. Sure, AI can cut costs and automate jobs, but guess what? Someone still has to feed it data, fix its mistakes, and guide its next move. Here, we are the teachers—teaching AI to become a better “person” in society than us 😄. If we’re not the ones driving the AI, we’re just cargo on its ride—and that’s not a good seat🙃. #AI #FutureOfWork #DataDriven #TechLeadership #Innovation

Choose AI first solutions, not solutions with retro-fitted AI afterthoughts 550+ companies have turned to RELEX Solutions when they needed AI to deliver measurable value, $billions of value since 2005. And this is just the start. #AI #supplychain #supplychainplanning

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

Can AI Teach Itself? With RAG + Synthetic Data, AI isn’t just processing information, it’s learning smarter, faster, and more autonomously. This shift is redefining how we think about model training and knowledge expansion. #AI #MachineLearning #RAG #SyntheticData #ArtificialIntelligence #FutureOfAI #TechInnovation

🐹 Chinchilla Scaling Law – Smarter AI Training 🚀 Bigger isn’t always better in AI. Balance is everything. DeepMind’s Chinchilla showed that balancing model size and data is the real key: 📏 20 training tokens per parameter = optimal performance. Why this matters: ✅ More data can beat just more parameters ✅ Smaller, well-trained models are cheaper & faster ✅ Better efficiency → better results 💡 The future of AI isn’t just about going bigger - it’s about going smarter. #AI #MachineLearning #ChinchillaScalingLaw #DeepMind #LLM #AIInnovation

Introducing EverestQ – a Humanoid LLM built from scratch. ⚡ 700 Billion Parameters ⚡ Faster than GPT-4 ⚡ Sharper, research-grade answers 📅 Launching 7th September – and it will be accessible to everyone, at zero cost. The future of AI is here — smarter, faster, and free. 🌍✨ we have cros phased 5th #EverestQ #LLM #AI #Innovation #700B #MadeFromScratch

Most teams will chase shortcuts with AI – we use it to rewire how work flows. That meant experimenting, swapping tools in and out, and learning when to lean on AI… and when to lean on ourselves. The big unlocks: - There’s no such thing as a static stack – iteration wins - The best results happen when AI reduces friction, not when it replaces effort - Successful AI adoption requires clear objectives for the tools What we learned is bigger than a single project. AI tools will make every studio faster. But the great ones will get smarter. The carousel below shares how we applied AI, what worked, and where we’ll push further next time.

We learned that steady gains with AI come from experimenting often and refining quickly. By swapping tools in and out and defining their role in the workflow, we removed friction and freed the team to focus on the work that mattered. Over time, those small shifts built confidence, consistency, and momentum across projects. Want to see how iteration drove impact? Look below:

AI became effective only once we gave it purpose. By clearly defining whether a tool was meant to speed things up or improve quality, support reviews or generate content, we gave the team a framework they could rely on. The result? Less friction, better collaboration, and outcomes we could confidently stand behind. Curious how clarity turned adoption into impact?