Why Large Language Models Give Different Answers

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

Language models don’t only generate answers—they sometimes hallucinate. 𝐎𝐩𝐞𝐧𝐀𝐈’𝐬 𝐧𝐞𝐰 𝐩𝐚𝐩𝐞𝐫, 𝐖𝐡𝐲 𝐥𝐚𝐧𝐠𝐮𝐚𝐠𝐞 𝐦𝐨𝐝𝐞𝐥𝐬 𝐡𝐚𝐥𝐥𝐮𝐜𝐢𝐧𝐚𝐭𝐞, dives deep into the statistical roots of these errors. Hallucinations happen when models are incentivized to guess rather than acknowledge uncertainty. Traditional evaluation methods prioritize accuracy alone, which unintentionally rewards confident but wrong answers. But the key insight is that hallucinations are 𝐧𝐨𝐭 𝐢𝐧𝐞𝐯𝐢𝐭𝐚𝐛𝐥𝐞. Instead of guessing, models can be trained to abstain when they are uncertain. This shift aligns well with 𝑶𝒑𝒆𝒏𝑨𝑰’𝒔 𝒑𝒓𝒊𝒏𝒄𝒊𝒑𝒍𝒆 𝒐𝒇 𝒉𝒖𝒎𝒊𝒍𝒊𝒕𝒚: 𝒂𝒅𝒎𝒊𝒕𝒕𝒊𝒏𝒈 “𝑰 𝒅𝒐𝒏’𝒕 𝒌𝒏𝒐𝒘” 𝒊𝒔 𝒃𝒆𝒕𝒕𝒆𝒓 𝒕𝒉𝒂𝒏 𝒄𝒐𝒏𝒇𝒊𝒅𝒆𝒏𝒕𝒍𝒚 𝒈𝒊𝒗𝒊𝒏𝒈 𝒕𝒉𝒆 𝒘𝒓𝒐𝒏𝒈 𝒂𝒏𝒔𝒘𝒆𝒓. The paper calls for a rethinking of evaluation frameworks—moving beyond accuracy-focused scoreboards to metrics that reward appropriate expressions of uncertainty. By changing how we measure success, we can encourage models that are more reliable, trustworthy, and ultimately safer. This is an important reminder for the broader AI community: 𝐫𝐞𝐝𝐮𝐜𝐢𝐧𝐠 𝐡𝐚𝐥𝐥𝐮𝐜𝐢𝐧𝐚𝐭𝐢𝐨𝐧𝐬 𝐢𝐬 𝐧𝐨𝐭 𝐣𝐮𝐬𝐭 𝐚 𝐭𝐞𝐜𝐡𝐧𝐢𝐜𝐚𝐥 𝐜𝐡𝐚𝐥𝐥𝐞𝐧𝐠𝐞, 𝐛𝐮𝐭 𝐚𝐥𝐬𝐨 𝐚 𝐦𝐚𝐭𝐭𝐞𝐫 𝐨𝐟 𝐡𝐨𝐰 𝐰𝐞 𝐝𝐞𝐬𝐢𝐠𝐧 𝐢𝐧𝐜𝐞𝐧𝐭𝐢𝐯𝐞𝐬. #genai #openai #llm #ds #ml #ai #generativeai

AI may be reaching its true “light bulb moment.” When large language models move beyond statistical word prediction into something that resembles meaning, we’re not just seeing better autocomplete. We’re witnessing an inflection point where complexity generates emergent understanding. This is the frontier: - From counting words → to creating semantic relationships. - From probability → to possible meaning. - From tools → to partners in thought. The question is not whether AI can calculate, but whether we are ready to face what happens when calculation starts to feel like comprehension. Psychology Today article - https://lnkd.in/dBrt4E6R #AI #ArtificialIntelligence #LLM #GenerativeAI #ResponsibleAI #FutureOfAI #EmergentIntelligence #Innovation #MachineLearning #Technology

𝐋𝐋𝐌𝐬 𝐚𝐫𝐞 𝐬𝐦𝐚𝐫𝐭, 𝐛𝐮𝐭 𝐝𝐨 𝐲𝐨𝐮 𝐤𝐧𝐨𝐰 𝐡𝐨𝐰 𝐭𝐡𝐞𝐲 𝐚𝐜𝐭𝐮𝐚𝐥𝐥𝐲 𝐬𝐞𝐚𝐫𝐜𝐡 𝐟𝐨𝐫 𝐭𝐡𝐞 𝐛𝐞𝐬𝐭 𝐚𝐧𝐬𝐰𝐞𝐫𝐬? Most people think AI just spits out one response. But in reality, Large Language Models (LLMs) explore multiple possible answers before deciding which one to present. 𝐓𝐡𝐞 𝐭𝐫𝐢𝐜𝐤 𝐥𝐢𝐞𝐬 𝐢𝐧 𝐭𝐡𝐞 𝐬𝐞𝐚𝐫𝐜𝐡 𝐬𝐭𝐫𝐚𝐭𝐞𝐠𝐲 𝐭𝐡𝐞 𝐦𝐞𝐭𝐡𝐨𝐝 𝐭𝐡𝐞 𝐦𝐨𝐝𝐞𝐥 𝐮𝐬𝐞𝐬 𝐭𝐨 𝐟𝐢𝐧𝐝 𝐭𝐡𝐞 𝐦𝐨𝐬𝐭 𝐚𝐜𝐜𝐮𝐫𝐚𝐭𝐞, 𝐮𝐬𝐞𝐟𝐮𝐥 𝐫𝐞𝐬𝐩𝐨𝐧𝐬𝐞. 𝐋𝐞𝐭’𝐬 𝐛𝐫𝐞𝐚𝐤 𝐝𝐨𝐰𝐧 𝐭𝐡𝐞 𝐭𝐡𝐫𝐞𝐞 𝐦𝐨𝐬𝐭 𝐜𝐨𝐦𝐦𝐨𝐧 𝐨𝐧𝐞𝐬 𝐢𝐧 𝐩𝐥𝐚𝐢𝐧 𝐄𝐧𝐠𝐥𝐢𝐬𝐡: 𝟏. 𝐁𝐞𝐬𝐭-𝐨𝐟-𝐍 The model generates multiple complete answers to the same question. A verifier then picks the best one out of the lot. Think of it as asking five friends the same question and choosing the most reliable answer. 𝟐. 𝐁𝐞𝐚𝐦 𝐒𝐞𝐚𝐫𝐜𝐡 Instead of generating full answers directly, the model expands step by step. At each step, it keeps the top-N possible continuations and discards the weaker ones. This allows the model to explore different paths and refine reasoning. 𝟑. 𝐋𝐨𝐨𝐤𝐚𝐡𝐞𝐚𝐝 𝐒𝐞𝐚𝐫𝐜𝐡 This takes Beam Search one step further. The model “𝐫𝐨𝐥𝐥𝐬 𝐨𝐮𝐭” a few steps ahead, evaluates the outcomes, and even rolls back if needed. It’s like playing chess thinking a few moves ahead before making your next move. 𝐖𝐡𝐲 𝐭𝐡𝐢𝐬 𝐦𝐚𝐭𝐭𝐞𝐫𝐬 These strategies are what make AI more than just text generators. They ensure answers are not only fluent but also factually stronger and logically consistent. The better we design these search strategies, the closer AI gets to human-like reasoning. 𝐖𝐡𝐢𝐜𝐡 𝐨𝐟 𝐭𝐡𝐞𝐬𝐞 𝐬𝐭𝐫𝐚𝐭𝐞𝐠𝐢𝐞𝐬 𝐝𝐨 𝐲𝐨𝐮 𝐭𝐡𝐢𝐧𝐤 𝐢𝐬 𝐜𝐥𝐨𝐬𝐞𝐬𝐭 𝐭𝐨 𝐡𝐨𝐰 𝐡𝐮𝐦𝐚𝐧𝐬 𝐫𝐞𝐚𝐬𝐨𝐧 𝐭𝐡𝐫𝐨𝐮𝐠𝐡 𝐩𝐫𝐨𝐛𝐥𝐞𝐦𝐬? ♻️ Repost this to help your network get started ➕ Follow Sivasankar Natarajan for more #AI #LLM #ArtificialIntelligence #AgenticAI

The secret sauce behind Transformers: Attention. 🤫🤖 So, what is Attention Mechanism: The 'secret sauce' of Transformers? In essence, it's a technique used in deep learning models that allows them to focus on specific parts of the input rather than the whole thing. It's like the model is giving certain parts its 'attention', hence the name. How does it work? 🧐 1️⃣ Every input in the sequence is connected to every output, with a unique weight. 2️⃣ These weights are calculated and adjusted depending on the relevance of each input to each output. 3️⃣ The model then decides where to 'pay attention' based on these weights. Why does it matter in Language Learning Models (LLMs) / General AI (GenAI)? The attention mechanism has greatly improved the performance of these models. It allows them to process longer sequences and maintain context, which is especially crucial in tasks like translation, text completion, and question answering. Want me to sketch how attention flows? Imagine being in an interview at one of the top tech companies where they ask you this question. How would you answer? #AttentionMechanism #DeepLearning #Transformers #AI #ML #GenAI

I'm writing an article on the importance of "taste" - perhaps the last bastion of human value in a world where AI can approximate or supplant human beings in all economic activities. So I was chatting with an AI to organize my thoughts. To all creators wondering if there's any point to the creative process if AI can do it all better, I asked my counterpart that very question. The response was so intelligent, I thought I would share. Which button would you press, at the end? The answer depends on your response to the "deeper question." AI offers efficiency, structure, clarity, and objectivity. You offer authenticity, and credibility to others, and discovery and personal satisfaction to yourself. If the point is to get something done, ask AI to do it. If the point is the doing, you do it. Be careful to consider what you might gain by slogging through it yourself. This is a good rehearsal for a world in which we don't really have much economic value relative to AI, at least not in the economic realms currently inhabited by humans. I want to be proud of my own writing output, in service of personal satisfaction. I also want to learn as I write. So I guess I'll have to write this one (and everything on summersoul.net) myself. sigh.

🚀 𝗔𝗜 𝗶𝘀 𝗺𝗼𝗿𝗲 𝘁𝗵𝗮𝗻 𝗷𝘂𝘀𝘁 𝗟𝗟𝗠𝘀 When we talk about AI today, most conversations stop at Large Language Models (LLMs). But the evolution of AI unfolds across four powerful stages: 1️⃣ RAG (Retrieval-Augmented Generation) – LLMs enriched with external knowledge for current & context-aware answers. 2️⃣ Fine-Tuning – Domain-specific training baked into the model for specialized expertise. 3️⃣ Agents – LLMs that can think → act → observe, chaining tasks with tools & APIs. 4️⃣ Agentic AI – Multiple agents coordinated by a planner to solve complex, multi-step, real-world problems. 💡 From answering questions → executing tasks → orchestrating workflows → managing entire ecosystems, this is the AI maturity curve. 👉 Question for you: Which stage do you think will disrupt your industry the most in the next 12–18 months? #AI #LLM #GenAI #RAG #Agents #AgenticAI #FutureOfWork

🚦 𝗘𝘀𝗰𝗮𝗽𝗶𝗻𝗴 𝗧𝘂𝗻𝗻𝗲𝗹 𝗩𝗶𝘀𝗶𝗼𝗻 𝗶𝗻 𝗟𝗟𝗠𝘀 Here's what researchers just discovered: When large language models reason step by step, they often fall into 𝘵𝘶𝘯𝘯𝘦𝘭 𝘷𝘪𝘴𝘪𝘰𝘯 — the first few words push them down a single (sometimes wrong) path. Just making that chain longer doesn’t magically fix it. 𝗧𝗵𝗲 𝘀𝗺𝗮𝗿𝘁𝗲𝗿 𝗺𝗼𝘃𝗲: 🔀 Run several shorter reasoning paths in parallel. Or simply ask the same question multiple times differently. 🔀 Combine the results (majority voting or summarization). 🔀 Get better answers without retraining the model. Think of it as “parallel brainstorming” for AI. Instead of betting everything on one long chain of thought, let multiple perspectives compete. Sometimes the best way forward isn't to think harder in one direction, but to think differently in many directions all at once. 🤖 𝗪𝗵𝗮𝘁’𝘀 𝘆𝗼𝘂𝗿 𝗴𝗼-𝘁𝗼 𝘁𝗿𝗶𝗰𝗸 𝗳𝗼𝗿 𝗴𝗲𝘁𝘁𝗶𝗻𝗴 𝗺𝗼𝗿𝗲 𝗿𝗲𝗹𝗶𝗮𝗯𝗹𝗲 𝗮𝗻𝘀𝘄𝗲𝗿𝘀 𝗳𝗿𝗼𝗺 𝗟𝗟𝗠𝘀? Source -> https://lnkd.in/ehH4yS7R #AI #GenAI #LLMs #TechStrategy #Parallelism

𝗟𝗟𝗠𝘀 𝘀𝗼𝘂𝗻𝗱 𝗯𝗿𝗶𝗹𝗹𝗶𝗮𝗻𝘁… 𝗯𝘂𝘁 𝗱𝗼 𝘁𝗵𝗲𝘆 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝗿𝗲𝗺𝗲𝗺𝗯𝗲𝗿 𝗮𝗻𝘆𝘁𝗵𝗶𝗻𝗴? Every time we chat with a Large Language Model (LLM), it forgets the past. Unless we provide the history again, it treats each conversation as completely new. This makes us wonder: 🔹If they can’t remember, can we really call them intelligent? The truth is, today’s LLMs are not “thinking machines.” They are advanced pattern predictors — generating answers based on training data, not true memory or understanding. That’s why the future focus is on: 🔹𝘼𝙙𝙙𝙞𝙣𝙜 𝙢𝙚𝙢𝙤𝙧𝙮 so LLMs can recall past interactions. 🔹𝘽𝙪𝙞𝙡𝙙𝙞𝙣𝙜 𝘼𝙄 𝙖𝙜𝙚𝙣𝙩𝙨 that combine reasoning, tools, and memory to act smarter. Until then, LLMs are impressive, but not truly intelligent — more like excellent imitators. #AI #LLM #ArtificialIntelligence #MachineLearning #FutureOfAI #GenerativeAI #Innovation

Day 65/100 Musings of the Week: You’re still smarter than AI As an avid user of large language models (LLMs), I believe the biggest risk we face isn’t the AI itself, it’s cognitive laziness. It’s easy to forget that AI is just a powerful tool and that it still needs wielding. Yes, LLMs may have the ability to generate impressive results, but they don’t possess our human insight, creativity, or judgment. Over time, we’ll begin to see a clear distinction between those who blindly follow AI outputs, and those who skillfully guide AI to bring their own unique vision to life. It’s tempting to let AI do all the thinking but now, more than ever, we need to think deeply, question critically, and apply our own perspective to steer these tools. If we don’t, we risk becoming just another echo in a sea of generic, bot-like outputs. So the next time you use Generative AI, remember: you’re still smarter than AI. Wishing us all a great weekend, and as always, remember that resting is as important as working hard #100DaysofLinkedIn #GenerativeAI