Sivasankar Natarajan’s Post

⚡ What is a Large Language Model (LLM)? Imagine Flash running through the world’s biggest library in seconds. He doesn’t memorize every page word-for-word… instead, he keeps the patterns of knowledge in his mind. That’s how an LLM works: It “reads” massive amounts of text (internet, books, articles). It compresses knowledge into billions of tiny switches (parameters). When you ask something, it doesn’t search Google—it predicts the next best word to create an answer. 💡 Simple: LLMs don’t store the internet, they rebuild answers from patterns they’ve learned. Today, they can already chat, code, see, hear, and use tools. But true AGI (Artificial General Intelligence)—an AI that can think and reason like humans—is still many years away. Most experts believe we’re on the path, but not quite there yet. --- ✨ Question for you: How far do you think we are from AGI—5, 10 years ??? #AI #LLM #AGI #GenerativeAI #MachineLearning

Did you know? Every powerful AI model you interact with—like GPT-4, Claude 4, or Gemini 2.5—started life as a humble base model. These base models (e.g., GPT-4 Base) are trained on massive datasets, absorbing patterns, language structures, and knowledge at scale. But they’re not quite ready for prime time yet. 🔧 The magic happens next: Model builders fine-tune these base models and apply Reinforcement Learning from Human Feedback (RLHF) to align them with human preferences, safety protocols, and conversational nuance. That’s how we go from raw intelligence to refined brilliance—transforming a base model into something like ChatGPT-4, capable of reasoning, empathy, and creativity. 📌 Even though these base models aren’t always available for public consumption, smaller open-source models often list their base versions. For example, you’ll find models like T5-Base on Hugging Face—great for experimentation and learning. #AI #MachineLearning #LLMs

Is there room for experts in the age of AI? Does one still need to read the full story book or shall we just jump straight to the epilogue? In the real world, knowledge builds upon knowledge. We start by identifying numbers, then we count before solving simple mathematical operations. Only after mastering the basics does one get to complexities. In the 'artificial world' however, you could PROVIDE THE ANSWER to a complex subject in a foreign language with the help of a LLM. How could you solve a riddle in a language whose subtleties you do not understand? I am among those who still consider #AI a tool. It will get you there faster if you know where you are going. Going back to my storybook analogy: In reading the whole book, you not only enjoy the story but also get to learn new phrases and improve your vocabulary. You might also get to learn about other cultural beliefs, religions and general life's philosophy from the characters. Then you become a refined person because your brain is more exposed. The future still requires experts in all fields. Nothing important will be built by those who outsource reasoning to machines. CAUTION: Do not argue with experts in public (because you read an AI summary), people will notice the difference

💡 LLMs are smart, but they forget fast. That’s where 𝐯𝐞𝐜𝐭𝐨𝐫 𝐝𝐚𝐭𝐚𝐛𝐚𝐬𝐞𝐬 step in. Think of them as the 𝐥𝐨𝐧𝐠-𝐭𝐞𝐫𝐦 𝐦𝐞𝐦𝐨𝐫𝐲 for Large Language Models. Instead of just matching keywords, they understand meaning by storing information as high-dimensional vectors. This unlocks some fascinating possibilities: ✨ Ask a chatbot about your company policies and get answers grounded in your own documents. ✨ Find insights across millions of research papers—not by exact words, but by concepts. ✨ Power recommendation systems that feel almost intuitive. Without vector databases, 𝐋𝐋𝐌𝐬 𝐚𝐫𝐞 𝐥𝐢𝐤𝐞 𝐛𝐫𝐢𝐥𝐥𝐢𝐚𝐧𝐭 𝐬𝐭𝐮𝐝𝐞𝐧𝐭𝐬 𝐰𝐢𝐭𝐡 𝐬𝐡𝐨𝐫𝐭-𝐭𝐞𝐫𝐦 𝐦𝐞𝐦𝐨𝐫𝐲 𝐥𝐨𝐬𝐬. With them, they become powerful problem-solvers that truly understand context. The future of AI isn’t just about bigger models—it’s about smarter memory. #AI #LLM #VectorDatabases #GenerativeAI #FutureOfWork

💡 Ever wondered why large language models sometimes give different answers to the same question? It's Because of thier Non-Deterministic nature. LLMs don’t always follow a fixed path—they generate responses based on probabilities. This makes them: 🎲 Creative and flexible 🎲 More human-like in conversation 🎲 Capable of surprising insights But… in certain scenarios, like retrieval-augmented generation (RAG) or enterprise systems where consistency is critical, this can be a challenge. Even small variations in output may ripple into downstream processes. So, you can reduce non-determinism 🌡️Lower the temperature 🔣 Standardize inputs 👨💻 Use prompt engineering and deterministic decoding strategies when needed At the end of the day, non-determinism gives LLMs their spark of creativity. The real skill is knowing when to let it shine—and when to tune it down for precision. 👉 What do you think #Ai #Rag #GenAI

🚀 Workflows vs Agents: How to Choose for Your LLM Solutions When building AI systems with large language models, one of the biggest decisions is: 👉 Should you use a workflow or an agent? Here’s a simple way to think about it (inspired by Anthropic’s excellent guide) 🔹 Workflows → Best for predictable, repetitive, structured tasks. Example: Auto-replying to customer emails with a standard message. ✅ Consistent, low-latency, cost-efficient. 🔹 Agents → Best for open-ended, dynamic, exploratory tasks. Example: Researching and summarizing the latest market trends. ✅ Adaptive, flexible, capable of multi-step reasoning. ⚠️ But higher latency and cost. 💡 Rule of thumb: If you know the exact path → Workflow. If the path is uncertain → Agent. Start simple. Often, a single LLM call + retrieval works better than overengineering an agent. Frameworks (LangGraph, Rivet, etc.) are helpful—but only after you understand the basics. ✨ Credit: Anthropic’s blog “Building Effective Agents” for the insights that inspired this post. #AI #LLM #Workflows #Agents #Anthropic #ArtificialIntelligence

Ever notice how AI can sound smart… but sometimes it’s just confidently wrong? That’s because it talks without really knowing. Recently, I was learning more about language models and learned something called 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹-𝗔𝘂𝗴𝗺𝗲𝗻𝘁𝗲𝗱 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻 (𝗥𝗔𝗚). Now imagine if it could pause, look things up, and then answer. That’s what Retrieval-Augmented Generation (RAG) does, it adds 𝗺𝗲𝗺𝗼𝗿𝘆, 𝗰𝗼𝗻𝘁𝗲𝘅𝘁, 𝗮𝗻𝗱 𝘁𝗿𝘂𝘁𝗵 𝘁𝗼 𝗔𝗜. The result? ✨ Fewer made-up answers ✨ More trust ✨ Real knowledge, not guesswork This shift isn’t just about AI -- it’s about building tools we can actually rely on. Because in the end, technology should help us work smarter, not just louder. #AI #RAG #FIASS #Pinecone #LLM #GPT #GEMINI

Everyone’s asking: is AI failing to deliver real business value? Here’s my take 👇 LLMs (not “AI magic,” but Large Language Models) are powerful — but only when used in the right context. Their DNA comes from language translation, which means they shine at transforming information: transcribing meetings, structuring messy notes, or reframing documents in new ways. But here’s the problem: • With thin datasets, they hallucinate. • With bias, they amplify it. • And worst of all, they rarely admit when they don’t know. So yes, the hype cycle is real. But dismissing LLMs outright misses the point. They’re not a silver bullet — they’re a scalpel. The key is choosing the right use case, not trying to hammer every problem with “AI.” What about you? Have you seen LLMs used effectively in your work — or mostly as overhyped experiments? #ai #llms #futuretech

🚨 𝐓𝐡𝐞 𝐀𝐈'𝐬 𝐓𝐰𝐨 𝐂𝐥𝐨𝐜𝐤𝐬: 𝐖𝐡𝐲 𝐘𝐨𝐮𝐫 𝐋𝐋𝐌 𝐂𝐚𝐧 𝐅𝐞𝐞𝐥 "𝐎𝐮𝐭 𝐨𝐟 𝐒𝐲𝐧𝐜" Ever presented an AI with a forward-looking article, only to have it dismissed as "speculative"? You're not imagining things. You're witnessing a fascinating challenge at the core of AI reasoning: the conflict of the "Two Clocks." 𝐂𝐥𝐨𝐜𝐤 1: 𝐓𝐡𝐞 𝐊𝐧𝐨𝐰𝐥𝐞𝐝𝐠𝐞 𝐋𝐢𝐛𝐫𝐚𝐫𝐲. Large Language Models are trained on vast datasets from the past. Their "worldview" is effectively frozen at the time their training ended. This makes them incredibly knowledgeable, but also inherently dated. 𝐂𝐥𝐨𝐜𝐤 2: 𝐓𝐡𝐞 𝐑𝐞𝐚𝐥-𝐓𝐢𝐦𝐞 𝐂𝐚𝐥𝐞𝐧𝐝𝐚𝐫. The same AI is also aware of the current date. It knows what day it is, but it has no lived experience or updated knowledge of the events between its training cut-off and today. 𝐓𝐡𝐞 𝐟𝐫𝐢𝐜𝐭𝐢𝐨𝐧 𝐡𝐚𝐩𝐩𝐞𝐧𝐬 𝐰𝐡𝐞𝐧 𝐰𝐞 𝐚𝐬𝐤 𝐚𝐧 𝐀𝐈 𝐭𝐨 𝐞𝐯𝐚𝐥𝐮𝐚𝐭𝐞 𝐧𝐞𝐰, 𝐟𝐨𝐫𝐰𝐚𝐫𝐝-𝐥𝐨𝐨𝐤𝐢𝐧𝐠 𝐢𝐧𝐟𝐨𝐫𝐦𝐚𝐭𝐢𝐨𝐧. 𝐈𝐭𝐬 𝐢𝐧𝐬𝐭𝐢𝐧𝐜𝐭 𝐢𝐬 𝐭𝐨 𝐟𝐚𝐜𝐭-𝐜𝐡𝐞𝐜𝐤 𝐭𝐡𝐞 𝐧𝐞𝐰 𝐝𝐚𝐭𝐚 𝐚𝐠𝐚𝐢𝐧𝐬𝐭 𝐢𝐭𝐬 𝐨𝐥𝐝, 𝐬𝐭𝐚𝐭𝐢𝐜 𝐥𝐢𝐛𝐫𝐚𝐫𝐲. If there's a mismatch (e.g., an article analyzing the impact of a technology that wasn't released pre-training), the AI can default to caution, labeling the new insight as "speculation" rather than "analysis." 𝐈𝐭'𝐬 𝐧𝐨𝐭 𝐚 𝐛𝐮𝐠; it's a fundamental challenge of reasoning. Humans constantly update their mental models and intuitively understand the difference between a news report, an opinion piece, and a strategic forecast. For AI, this is a sophisticated skill it's still learning. The next frontier for AI isn't just about knowing more information. It's about developing the wisdom to understand context, intent, and the nuanced ways we talk about the future. We're teaching the machine not just to read the calendar, but to truly understand what it means to look ahead. Have you run into this 'AI time lag' yourself? I'd be interested to hear your experiences in the comments. 👇 #ArtificialIntelligence #AI #LLM #MachineLearning #FutureOfTech #Gemini

Quick thought for my network: We need to be more precise in our terminology. Large Language Models (LLMs) are not "AI". There is no intelligence in these models. They are interesting technology, powerful pattern predictors, but they are not thinking beings. Let's discuss the technology accurately to better manage expectations and drive real innovation. P.s: This post was 100% written by a human. N.b: It's ok to edit you thoughts with LLMs, just review and have the final word about what you produce. #LLM #TechInnovation #MachineLearning #LargeLanguageModels