How Gryps' AI search tool helps construction professionals find the right information

Industry data is fragmented and ALWAYS comes from different sources. Read our latest post on how Gryps creates the #owners knowledge graph to combine structured and unstructured data to power our #ai #search

👻👽The Magic of “Mixture-of-Experts” (MoE) 🧙♂️🧙♀️ Ever wondered how new AI models like GPT-5, Grok 4, or Gemini 2.5 Pro are becoming so powerful without costing a fortune to run? Meet Mixture-of-Experts (MoE) — a game-changing technique that makes Large Language Models (LLMs) more efficient, scalable, and specialized. 🧠✨ 🔹 What Is Mixture-of-Experts (MoE)? Think of MoE like a team of specialists. Instead of asking every team member to work on every task (like older AI models did), the system only calls the right experts for the job. 🧩 How it works: The model has many “experts” — small specialized sub-models (math, code, writing, etc.) A “gating network” acts like a manager, picking the best experts for the task Only those experts are activated → saving compute, boosting performance 👉 Result: The model becomes faster, cheaper, and smarter at focused tasks. 🔹 Real-World Examples (as of 2025) ModelDeveloperMoE?NotesGPT-5OpenAI✅ SpeculatedMassive scale, likely dynamic routingGrok 4xAI✅ ConfirmedMulti-agent MoE, very efficientGemini 2.5 ProGoogle✅ ConfirmedDesigned for efficient scalingClaude 4Anthropic❌Probably dense (no MoE yet)DeepSeek-V3DeepSeek✅ Confirmed671B total, 37B active per token 🔹 Why It Matters ✅ Efficiency: Uses less compute → faster and greener AI ✅ Scalability: Add more experts without slowing down ✅ Specialization: Experts learn unique skills → better accuracy ⚠️ Challenges Routing can sometimes misfire (wrong expert chosen) Requires more memory to store all experts Harder to interpret why a certain expert was picked 💡 TL;DR Mixture-of-Experts = Specialized AI teamwork. Instead of using the whole brain every time, the model just activates the smartest neurons for the task at hand. Smarter use of compute = better performance for less cost. That’s the future of AI — intelligent specialization. 🌍💻 👉 Curious takeaway: The next time you hear about “GPT-5” or “Grok 4,” know that there might be hundreds of tiny experts behind the scenes — working together to make your AI conversations faster and sharper than ever. Asharib Ali | Naeem H. | Ameen Alam | Daniyal Nagori | Muhammad Qasim | #AI #MachineLearning #Innovation #MoE #LLM #ArtificialIntelligence #GPT5 #DeepLearning #TechExplained #FutureOfAI

I’ve been exploring the evolving world of AI lately, and it’s fascinating how every type of AI has its own mindset — different tools, different purposes, completely different ways of working. Here’s a simplified take on what I’ve learned so far. 1.Generative AI – The Creator This is the kind of AI that creates — whether it’s text, code, art, or music. Tools used: GPT, Gemini, Claude, DALL·E, Midjourney Used for: ·  ChatGPT generating text or ideas in real time. ·  GitHub Copilot writing code as developers type. 2. Multimodal AI – The All-Rounder AI that can understand multiple formats at once — text, image, video, and even audio. Tools used: GPT-4V, Gemini, CLIP, LLaVA Used for: · Google Gemini interpreting an image or chart and explaining it in context. · AI assistants summarizing scanned documents or screenshots. 3. Reasoning AI – The Thinker AI that doesn’t just predict — it reasons step by step to solve complex problems. Tools used: Claude 3.7, DeepSeek-R1, OpenAI o1, Mistral-Large Used for: · Solving logic or math problems with step-by-step thinking. · Debugging code or analyzing data through structured reasoning. 4. RAG (Retrieval-Augmented Generation) – The Researcher Combines generation with real-time retrieval — looking up external information before answering. Tools used: LangChain, LlamaIndex, Pinecone, Qdrant, Weaviate Used for: · Chatbots that pull answers from company documents. · AI tools that summarize the latest research papers or reports accurately. 5. Agentic AI – The Doer (and the Latest Game-Changer) This is the newest wave of AI — and one of the most exciting ones yet. Agentic AI doesn’t just reply — it acts. It can plan, reason, use tools, connect APIs, and even work with other AIs to complete tasks end to end. Tools used: AutoGen, CrewAI, LangGraph, OpenDevin, Meta’s Agentic Frameworks Used for: · AI systems that schedule meetings, draft emails, or analyze data automatically. · Multi-agent setups where one AI researches, another writes, and a third reviews — all on their own. The next wave of AI is already forming — with Embodied AI (robots that physically act in the world), Edge AI (smart assistants running directly on devices), and Emotional or Empathetic AI (systems that can sense and respond to human emotion). AI isn’t just one thing anymore — it’s an entire ecosystem of creators, thinkers, and doers. Exploring about it has made me even more curious about what’s coming next. #ArtificialIntelligence #MachineLearning #AgenticAI #RAG #GenerativeAI #ReasoningAI #AIInnovation #TechTrends #LearningJourney

You may have heard the term “RAG” in AI conversations, but what does it actually mean? RAG, or Retrieval-Augmented Generation, lets AI look things up before answering, rather than relying only on what it memorised during training. This makes their outputs traceable, verifiable, and grounded in real data/facts. This is a crucial step in creating trust in what AI tells you, whether it’s for customer support, internal reports, or business insights. It is essentially a way to make AI more accountable and verifiable. Learn more and see practical examples here: https://lnkd.in/eMBGqk2J

What Is RAG? How Retrieval-Augmented Generation Is Changing the Way AI Understands Data #RAG, short for Retrieval-Augmented Generation, is a technique that allows a language model to pull in external information before generating a response. In simple terms, instead of relying only on what it “remembers,” the AI can now look things up in real time. For example, if you ask a company chatbot: “How many vacation days do I have left this year?” Without RAG, the model would have no clue — that data isn’t in its training set. https://lnkd.in/gePJcsmz

Confused by all the AI buzzwords? Let's make it simple. If you're diving into Generative AI, you'll hear LLM, RAG, and Agents everywhere. Understanding the difference is key to knowing what's possible. Here is the most intuitive explanation I use: 1️⃣ LLMs (Large Language Models) Think of the LLM as the "Brain" 🧠. It's an incredibly powerful reasoning engine trained on a massive, static snapshot of the internet. It's a master of language, creativity, and pattern recognition. ✅ Strengths: • Can write, summarize, translate, and code. • Generates human-like, creative, and coherent text. • Understands complex concepts and ideas. ❌ Limitations: • Its knowledge is "frozen in time." It has a cutoff date and knows nothing about recent events. • It cannot access live data (like news or your company's database). • Prone to "hallucinating" (confidently making up facts). Example: Ask a base LLM, "What's the weather today?" It can't tell you. It can only explain what weather is. 2️⃣ RAG (Retrieval-Augmented Generation) This gives the Brain a "Library" 📚. RAG connects the LLM to an external, up-to-date knowledge source. It works in two steps: first, it Retrieves relevant, factual information (from your documents, a database, or the web), and then it uses that info to Generate an answer. ✅ Strengths: • Provides current, accurate, and verifiable answers. • Dramatically reduces hallucinations by "grounding" the LLM in facts. • Perfect for domain-specific knowledge (e.g., answering questions about your internal company data). ❌ Limitations: • Can be slightly slower because of the extra "retrieval" step. • The quality of the answer is only as good as the quality of the information in the "library." Example: A RAG-powered customer bot can look up your actual order status and tell you "Your package is out for delivery," not just give a general answer. 3️⃣ Agents This gives the Brain + Library a set of "Hands" 👐. An Agent doesn't just answer your question; it completes your task. It uses the LLM to reason, create a multi-step plan, and then execute that plan by using "tools" (like APIs, web browsers, or other software). ✅ Strengths: • Can perform complex, multi-step actions in the real world. • Can make decisions, use software, and interact with external systems. • The foundation for true autonomous AI assistants. ❌ Limitations: • Significantly more complex to build, manage, and secure. • Requires strong guardrails to prevent it from taking incorrect or unintended actions. 🌟 To Summarize • LLM = 🧠 The Brain (Thinks & writes) • RAG = 🧠 + 📚 The Brain + a Library (Looks up facts, then thinks & writes) • Agent = 🧠 + 📚 + 👐 The Brain + Library + Hands (Looks up facts, thinks, and acts) Which of these are you most excited about? #GenerativeAI #AI #LLM #RAG #AIAgents #ArtificialIntelligence #TechExplained #Innovation #Business

Confused by all the AI buzzwords? Let's make it simple. If you're diving into Generative AI, you'll hear LLM, RAG, and Agents everywhere. Understanding the difference is key to knowing what's possible. Here is the most intuitive explanation I use: 1️⃣ LLMs (Large Language Models) Think of the LLM as the "Brain" 🧠. It's an incredibly powerful reasoning engine trained on a massive, static snapshot of the internet. It's a master of language, creativity, and pattern recognition. ✅ Strengths: • Can write, summarize, translate, and code. • Generates human-like, creative, and coherent text. • Understands complex concepts and ideas. ❌ Limitations: • Its knowledge is "frozen in time." It has a cutoff date and knows nothing about recent events. • It cannot access live data (like news or your company's database). • Prone to "hallucinating" (confidently making up facts). Example: Ask a base LLM, "What's the weather today?" It can't tell you. It can only explain what weather is. 2️⃣ RAG (Retrieval-Augmented Generation) This gives the Brain a "Library" 📚. RAG connects the LLM to an external, up-to-date knowledge source. It works in two steps: first, it Retrieves relevant, factual information (from your documents, a database, or the web), and then it uses that info to Generate an answer. ✅ Strengths: • Provides current, accurate, and verifiable answers. • Dramatically reduces hallucinations by "grounding" the LLM in facts. • Perfect for domain-specific knowledge (e.g., answering questions about your internal company data). ❌ Limitations: • Can be slightly slower because of the extra "retrieval" step. • The quality of the answer is only as good as the quality of the information in the "library." Example: A RAG-powered customer bot can look up your actual order status and tell you "Your package is out for delivery," not just give a general answer. 3️⃣ Agents This gives the Brain + Library a set of "Hands" 👐. An Agent doesn't just answer your question; it completes your task. It uses the LLM to reason, create a multi-step plan, and then execute that plan by using "tools" (like APIs, web browsers, or other software). ✅ Strengths: • Can perform complex, multi-step actions in the real world. • Can make decisions, use software, and interact with external systems. • The foundation for true autonomous AI assistants. ❌ Limitations: • Significantly more complex to build, manage, and secure. • Requires strong guardrails to prevent it from taking incorrect or unintended actions. 🌟 To Summarize • LLM = 🧠 The Brain (Thinks & writes) • RAG = 🧠 + 📚 The Brain + a Library (Looks up facts, then thinks & writes) • Agent = 🧠 + 📚 + 👐 The Brain + Library + Hands (Looks up facts, thinks, and acts) Which of these are you most excited about? hashtag #GenerativeAI hashtag #AI hashtag #LLM hashtag #RAG hashtag #AIAgents hashtag #ArtificialIntelligence hashtag #TechExplained hashtag #Innovation hashtag #Business

AI terms get thrown around so much that it’s easy to lose the meaning. This breakdown makes it clear: • LLMs = the brain • RAG = the brain + a library • Agents = the brain + a library + hands The visual makes the differences click. If you’ve been trying to explain this to your team or leadership, this is a clean way to do it. Reposting because it’s actually helpful.

Shaf Shafiq That’s a very powerful explanation! One thing to remember is: I come first, the audience second. Just because Agents can technically do everything that a base LLM or a RAG system can do, it doesn’t mean we should always use an Agent. 😅 We need to keep in mind: - The complexity of our design - The resources available - All other practical aspects of our system The goal is to choose the most optimized solution for our specific case, not necessarily the “best” or most capable one in absolute terms. Sometimes a simple LLM or a RAG setup is more efficient, easier to maintain, and perfectly sufficient for the task — no need to overcomplicate with a full Agent if it’s not required.

Are your AI tools operating in silos? Businesses are realizing that large language models aren’t enough on their own, especially when data lives across disconnected systems. Learn how the Model Context Protocol (#MCP) could be the missing piece of the puzzle that lets #AI agents work alongside people, securely access tools, and drive automated, multistep workflows. https://ow.ly/C0Qz50XnwiI