Why Small Language Models Will Dominate Agentic AI

🚨 GPT-5 & AI Hallucinations: What’s Real, What’s Not Even the most advanced AI, like GPT-5, isn’t immune to hallucinations, when a model generates information that sounds correct but is actually wrong. 🔍 Why It Happens LLMs (like GPT-5) don’t “know” facts. They predict the next likely word, not the truth. ➡️ When uncertain, they may “fill in gaps” with fabricated details. 📊 What’s New with GPT-5 ✔️ 45–80% fewer hallucinations compared to GPT-4 ✔️ 67% fewer coding hallucinations in benchmark tests ⚠️ But OpenAI itself stresses: hallucinations are still an issue 🛠️ The Solution: RAG (Retrieval-Augmented Generation) RAG reduces hallucinations by: 🔗 Connecting GPT-5 to real, external data sources 📑 Grounding answers in verified documents 👀 Providing traceability so users can check sources 💡 Why It Matters • Healthcare: Prevent harmful misdiagnoses • Business: Protect customer trust • Research: Avoid fake citations ✨ GPT-5 is a leap forward, but not flawless. Pairing it with RAG + human review is the way to build trustworthy AI You can learn more: https://bit.ly/42rYlt0 #GPT5 #AIHallucinations #GenerativeAI #ResponsibleAI #RAG

🧠 The Evolution of AI: From Rules to Reasoning to Real-Time Agency: I think we all may agree that (AI) Artificial Intelligence has come a long way since its humble beginnings. Let's take a short journey back and move forward to the present. 🔹 Early AI (Symbolic/Rule-Based) Rigid, handcrafted rules and logic trees — powerful yet narrow, unable to adapt beyond their programmed confines. 🔹 Machine Learning Era Systems learned patterns from data, allowing for predictive insights and adaptive decision-making — ushering in automation at scale. 🔹 Generative AI Large Language Models (LLMs) reshaped what was possible, enabling human-like text, image, and code generation — a true leap in creativity and contextual understanding. 🔹 Agentic AI (The Now & Next) We are entering an era of autonomous AI agents — able to plan, reason, execute multi-step tasks, and collaborate across platforms. This is not just AI responding — it’s AI acting. At ProInception, we help organizations navigate this shift in automation, iPaaS, and AI advances in order to unlock what’s next. 📍 Yesterday, AI was rules. 📍 Today, it’s generative. 📍 Tomorrow, it’s agentic. Are you ready? Let's connect and converse: Michael@proinception.com #AI #GenAI #AgenticAI #Automation #DigitalTransformation #ProInception #MachineLearning #RPA #iPaaS

Master the art of getting the best from AI 🤖 Prompt engineering is the key skill for crafting effective inputs that yield high-quality outputs from large language models. Here is how to write powerful prompts: 🧠 Be specific and provide context: Vague requests get vague results. Give clear, detailed instructions and background information to guide the AI. 🎭 Define a Persona: Tell the AI what role to play (e.g., "Act as a senior marketing director") to align its tone and expertise with your needs. 📋 Specify the Format: Do you need a bulleted list, a table, or a paragraph? Defining the structure ensures you get a usable response. 🚧 Set Constraints: Give boundaries like word count, tone, or what to avoid to keep the output focused and on-brand. 💡 Pro Tip: Use few-shot prompting by providing examples of the desired style or format. This is incredibly effective for consistent results. Remember, prompt crafting is iterative. Refine and build upon your prompts to unlock the full potential of generative AI. #PromptEngineering #AI #GenerativeAI #TechSkills #Innovation

6 AI trends you'll see more of in 2025 🤖 💻 💼 1. AI models will become more capable and useful 2. Agents will change the shape of work 3. AI companions will help you in your everyday life 4. AI will become more resource efficient over time 5. Measurement and customization will be key to building AI responsibly 6. AI will accelerate scientific discoveries https://lnkd.in/e985ZPtv

"Unlocking AI Potential: Prompt Engineering 🚀 Prompt engineering is revolutionizing the way we interact with AI models. By crafting precise, well-designed prompts, we can tap into the full potential of language models and generate high-quality outputs. What makes a good prompt? 🤔 - Clear objectives - Specific context - Relevant details Mastering prompt engineering can help you: - Improve model performance - Enhance creativity - Streamline workflows Whether you're a developer, researcher, or enthusiast, prompt engineering is an essential skill to have in your AI toolkit. 💻 Start experimenting with prompts today and discover the power of AI! 💡 #PromptEngineering #AI

Day 2 – AI, ML & Generative AI: Clearing the Fog Everywhere we look, terms like AI, ML, and Generative AI are thrown around. But are they the same? Not really. Here’s the simple breakdown: Artificial Intelligence (AI): The big umbrella — machines that mimic human intelligence (reasoning, problem-solving, decision-making). Machine Learning (ML): A subset of AI — machines learn patterns from data and improve over time (e.g., spam filters, recommendation engines). Generative AI (GenAI): A newer subset — not just learning, but creating (text, images, code, music). Think ChatGPT, DALL·E, or GitHub Copilot. In short: AI = Intelligence | ML = Learning | GenAI = Creating The real magic is how these build on each other to transform the way we work, innovate, and even express creativity. As we move deeper into this 30-day journey, I’ll keep simplifying AI concepts so they’re not just buzzwords, but tools we can actually understand and apply. Join me in this learning adventure, where sharing knowledge becomes a two-way street of growth and enlightenment. Let's share in comments when you hear “AI,” what’s the first thing that comes to mind — intelligence, learning, or creativity? #30DaysOfAIWithSudipta #ArtificialIntelligence #MachineLearning #GenerativeAI #AIInnovation #FutureOfWork

"We want to use AI." But what they really needed was simple math. A few weeks back, one of our clients approached us to build an AI-based solution. They were an educational institution. Their goal was to track inventory leak (amongst other things). Now, sure, we could’ve trained a model to detect anomalies. But the outcome could be achieved with basic math. A simple look at standard deviation across inventory consumption was enough to flag concerns. No AI required. No inflated budgets. No tech for the sake of tech. We scrapped the AI idea and built a clean, insight-driven dashboard instead. Fast. Simple. Useful. And the client loved it. Because good AI consulting is about saying no to unnecessary AI. It’s easy to sell the hype. But real value comes from solving the right problem with the right tool. Ever had to say no to a client for their own good? Tell me your story 👇 #AIforGood #AI #ArtificialIntelligence #BusinessCaseStudy

Explain your AI bot your world by using simple sentences, which it is able to understand! Use simple triple structures like... Subject, Predicate, Object Triples?🤔 I remember that I have heard this in context of the Resource Description Framework resp. Knowledge Graph. "... What This Means for Organizations For organizations investing in AI, GenAI, or self-service analytics, semantics is a critical foundation for long-term success. ..." https://lnkd.in/eexNgWVB

The real shift in AI isn’t about the model. It’s about the conversation. When GPT-5 launched, OpenAI tried something bold: one universal model to replace them all. Clean, simple, scalable. But in practice, it broke things. People missed the unique strengths of the older models. Workflows collapsed, costs went up, and frustration grew. The lesson? Technology isn’t the bottleneck anymore. We are. With GPT-5 and beyond, the difference between average and exceptional results won’t come from choosing the right model. It will come from learning how to communicate with AI. Not vague one-liners. Not “magic prompts.” But structured conversations. Clear context. Detailed instructions. Collaboration. In this new era, it’s not just what you ask. It’s how you ask. #ArtificialIntelligence #FutureOfWork #AILeadership #PromptEngineering #GenerativeAI #TechStrategy

Not all AI is intelligent. Understanding the full stack of machine cognition is the real unlock. LLM ≠ Generative AI ≠ AI Agents ≠ Agentic AI. We often talk about “AI” as if it’s one uniform capability—but the reality is layered, nuanced, and evolving fast. If you’re building anything that claims to be intelligent, you need to understand the stack of cognition powering it. Inspired by Brij Kishore Pandey’s breakdown, here’s the progression: ● Large Language Models (LLMs) – The raw predictive engines—good at next-token guesswork, but passive. They generate, but don’t reason. ● Generative AI – Applies LLMs to content—code, text, image. Still lacks autonomy. It creates outputs, not outcomes. ● AI Agents – These introduce goal orientation. They retrieve, reason, and execute. Less about content, more about task flow. ● Agentic AI – This is where systems initiate, adapt, and self-organize. They plan. They prioritize. They behave. Why does this matter? Because product builders often stop at GenAI—and miss the deeper opportunity: systems that can think and act. In our work with AI-enabled hiring, we’re shifting from static recommendations to agentic scoring, real-time validation, and continuous learning—because matching talent is no longer a one-shot task. It’s not just about prompts. It’s about orchestration, adaptability, and autonomy. Where are you on the AI stack, and what layer are you building toward? #AgenticAI #LLMs #SkillGraph #UpTechSolution #upstar