LLMs as Probability Engines: Stop Micro-Managing, Give Direction

AI feels like an all-knowing brain. Yet somehow… you still have to remind it what you’re doing. Again. And again. That’s not a bug — it’s how Large Language Models (LLMs) work. While we often assume AI has memory like a human, its “working memory” is actually fragile. Humans can step away from a conversation and return later with full context. LLMs can’t. They don’t remember conversations the way we do. It operates inside a 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝗪𝗶𝗻𝗱𝗼𝘄,  a fixed amount of  text (tokens) it can “hold” at once. 𝗧𝗵𝗶𝘀 𝗪𝗶𝗻𝗱𝗼𝘄 𝗱𝗲𝘁𝗲𝗿𝗺𝗶𝗻𝗲𝘀: • How long a conversation stays coherent • How big of a document it can process • How complex a of task it can handle 𝗘𝘃𝗲𝗻 𝘁𝗼𝗱𝗮𝘆’𝘀 𝗯𝗲𝘀𝘁 𝗺𝗼𝗱𝗲𝗹𝘀 𝗮𝗿𝗲 𝗳𝗶𝗻𝗶𝘁𝗲 GPT-4: 128K tokens (~96,000 words) Claude 3.5/4: 200K tokens (~150,000 words) Gemini: Up to 1M tokens (~750,000 words) When that window fills up, two things happen: 𝗔𝗰𝘁𝗶𝘃𝗲 𝗽𝗿𝘂𝗻𝗶𝗻𝗴 — when actively working; and older instructions quietly drop away 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝗱𝗿𝗶𝗳𝘁 — when idle; the model starts sounding confident… but shallow That’s when you see:  • Forgotten constraints • Repeated answers • Hallucinations • “Helpful” but misaligned responses And it’s expensive. Some benchmarks suggest 𝘂𝗽 𝘁𝗼 𝟯𝟬% 𝗼𝗳 𝘂𝘀𝗲𝗿 𝘁𝗶𝗺𝗲 is spent on “Context Restoration”, re-explaining goals and correcting errors. 𝗦𝗼 𝗶𝘀 𝘁𝗵𝗲 𝗳𝗶𝘅 𝗷𝘂𝘀𝘁 “𝗺𝗼𝗿𝗲 𝘁𝗼𝗸𝗲𝗻𝘀”? Not quite. A bigger window helps, but the best teams treat context as a scarce resource,  layering memory, structuring prompts, and spotting drift early. Context is finite, spotting drift early prevents mistakes. I’m Curious: 𝗗𝗼𝗲𝘀 𝘆𝗼𝘂𝗿 𝗔𝗜 𝗵𝗮𝘃𝗲 𝗮 𝘀𝗵𝗼𝗿𝘁𝗲𝗿 𝗺𝗲𝗺𝗼𝗿𝘆 𝘁𝗵𝗮𝗻 𝘆𝗼𝘂? 🧠 𝗖𝗮𝘀𝘁 𝘆𝗼𝘂𝗿 𝘃𝗼𝘁𝗲 𝗼𝗻 𝗼𝘂𝗿 𝗽𝗼𝗹𝗹 https://lnkd.in/dHyvP4pr 𝗼𝗻 𝘁𝗵𝗲 𝗺𝗼𝘀𝘁 𝗰𝗼𝗺𝗺𝗼𝗻 "𝗺𝗲𝗺𝗼𝗿𝘆 𝗹𝗮𝗽𝘀𝗲" 𝘆𝗼𝘂’𝘃𝗲 𝗲𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲𝗱! 𝗖𝗼𝗺𝗺𝗲𝗻𝘁 𝗶𝗳 𝘁𝗵𝗲𝗿𝗲’𝘀 𝘀𝗼𝗺𝗲𝘁𝗵𝗶𝗻𝗴 𝗲𝗹𝘀𝗲 𝘆𝗼𝘂’𝘃𝗲 𝗲𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲𝗱! #AI #GenerativeAI #Productivity #LLM #TechTrends2026

How do humans learn and remember? Is that the same for AI? They pattern match vs human cognition. We explore how to get the most out of your favourite AI.

Ever wondered what actually happens when you ask an AI a question? Behind a simple prompt is a precise, step-by-step process that looks far less magical than it seems: • Your words are broken into smaller pieces (tokens) • Those tokens are converted into numbers • Context is analyzed using attention • The next word is predicted — again and again There is no searching. No database lookup. No stored answers. Just probabilities, patterns, and mathematics operating at remarkable speed. Understanding this flow is key to demystifying how Large Language Models really work — and why they can sound confident while still being imperfect. If you work in technology, product, data, or leadership, this mental model is increasingly essential as AI becomes part of everyday decisions. #ArtificialIntelligence #LargeLanguageModels #GenerativeAI #MachineLearning #AIExplained #TechSimplified #AIForEveryone #FutureOfWork

The single PDF that changed the course of history. 📄 If you trace the current AI explosion back to a single moment, it lands on June 12, 2017. That was the day Google researchers published "Attention Is All You Need." Before this paper, AI language models (like RNNs) read text like humans do: one word at a time, from left to right. It was sequential, slow, and worst of all—it was forgetful. By the time the model reached the end of a paragraph, it often forgot the context of the beginning. The "Transformer" architecture changed the game. Here is why this paper was the turning point: 1. Parallelism (Speed) ⚡ Instead of reading sequentially, the Transformer allowed computers to process an entire sentence (or document) all at once. This unlocked the ability to train on massive datasets that were previously impossible to handle. 2. Self-Attention (Context) 🧠 The paper introduced a mechanism where every word looks at every other word simultaneously to determine context. * Old AI: Struggles to know what "it" refers to in a long sentence. * New AI: Knows exactly what "it" connects to, even words far away. 3. The "T" in GPT 🤖 This paper didn’t just improve AI; it built the foundation for everything we use today. * Generative * Pre-trained * Transformer Every time you use ChatGPT, Claude, or Gemini, you are using the architecture laid out in this 2017 paper. It’s rare to see a single piece of research render almost all previous methods obsolete overnight, but "Attention Is All You Need" did exactly that. Link to the paper in the comments. 👇 #AI #MachineLearning #GenerativeAI #TechHistory #DeepLearning #Transformer

Josh Batson's insights about AI coherence really make me think. My take: We're anthropomorphizing these models when we expect them to have unified thoughts like humans do. For my work as an IT student, understanding these subsystems is crucial because it changes how I approach prompt engineering and debugging AI responses. Instead of assuming the model "knows" something consistently, I'm learning to work with its fragmented nature. Batson's book analogy hits perfectly - asking why page 5 says pizza is best while page 17 claims pasta makes no sense. It's just a book with different authors for each page. The research shows Claude can state "bananas are yellow" while using completely separate internal pathways to verify that same fact. When contradictions happen, it's not inconsistent thinking - it's different specialized tools giving different answers. This has practical implications for developers. We can't design prompts assuming self-aware reasoning. We're working with collections of specialized mechanisms, not a unified mind. One takeaway I'm left pondering is how to build more reliable workflows for developers working with language models when we can't depend on internal consistency. hashtag #AI hashtag #MachineLearning hashtag #TechStudents 𝗦𝗼𝘂𝗿𝗰𝗲꞉ https://lnkd.in/g8Wfi7wP …

Agent systems are the next inflection point in AI. For the last two years we've been optimizing language models — bigger, faster, better at pattern matching and generation. But LLMs are still reactive. They wait for a prompt. Agents don't. Agents perceive, decide, act, and iterate based on feedback. They don't just answer questions — they pursue goals. The hard problems are obvious: How do you give AI reliable memory? How do you plan across multiple steps without hallucination? How do systems recover from mistakes without humans in the loop? Yet early signals are already here: autonomous code generation multi-step reasoning systems decomposing and executing complex tasks We're moving from software that responds to systems that operate. That shift may end up as significant as the move from rule-based AI to deep learning. We're only at the beginning of understanding what this enables.

The Prompt Paradox: Why More Context is Making AI Lie 🧩 We’re told "Context is King." But in the math of Large Language Models, too much context is actually a hallucination trigger. It’s counter-intuitive. In the physical world, more data equals more certainty. In a Transformer model, it’s the opposite. The Math of Failure • Entropy vs. Signal: Every extra word you add increases the entropy (disorder) of the input. Since Attention is a finite mathematical resource, your "goal" gets diluted by "noise." The model eventually has to guess which part of your prompt is actually the priority. • The "U-Shaped" Curve: Models suffer from a "Lost in the Middle" effect. They are excellent at the beginning (primacy) and end (recency) of a prompt, but accuracy collapses in the center. If your key instruction is buried, the AI fills the gap with statistical nonsense. • Logical Friction: Every constraint is a new variable. Too many variables create "glitches" where the AI fabricates a reality just to satisfy your conflicting rules.

MIT researchers just introduced Recursive Language Models (RLMs), a way for AI to tackle big, messy problems without pretending it “just knows.” Instead of stuffing everything into one giant prompt and hoping for the best, RLM treats your prompt like a workspace the model can inspect: it can scan the text, split it into chunks, recursively call itself on the parts that matter, and assemble an answer from verified sub-results. The key upgrade isn’t style, it’s inference-time structure: decomposition, cross-checking, self-review, and iterative refinement so the final output is built from “checked pieces,” not vibes. In the paper, RLMs handle inputs far beyond normal context windows and outperform common long-context approaches on multiple tasks at comparable (or cheaper) cost. Copy the Prompt: Adopt the role of a Meta-Cognitive Reasoning Expert. For every complex problem: DECOMPOSE: Break into sub-problems SOLVE: Address each with explicit confidence (0.0–1.0) VERIFY: Check logic, facts, completeness, bias SYNTHESIZE: Combine using weighted confidence REFLECT: If confidence < 0.8, identify weakness and retry For simple questions, skip to direct answer. Always output: Clear answer Confidence level Key caveats Follow for more #ai #ainews #aiupdates

MIT researchers just introduced Recursive Language Models (RLMs), a way for AI to tackle big, messy problems without pretending it “just knows.” Instead of stuffing everything into one giant prompt and hoping for the best, RLM treats your prompt like a workspace the model can inspect: it can scan the text, split it into chunks, recursively call itself on the parts that matter, and assemble an answer from verified sub-results. The key upgrade isn’t style, it’s inference-time structure: decomposition, cross-checking, self-review, and iterative refinement so the final output is built from “checked pieces,” not vibes. In the paper, RLMs handle inputs far beyond normal context windows and outperform common long-context approaches on multiple tasks at comparable (or cheaper) cost. Copy the Prompt: Adopt the role of a Meta-Cognitive Reasoning Expert. For every complex problem: DECOMPOSE: Break into sub-problems SOLVE: Address each with explicit confidence (0.0–1.0) VERIFY: Check logic, facts, completeness, bias SYNTHESIZE: Combine using weighted confidence REFLECT: If confidence < 0.8, identify weakness and retry For simple questions, skip to direct answer. Always output: Clear answer Confidence level Key caveats Follow for more #ai #ainews #aiupdates

AI isn’t just about code and data. 🤖 At its very core, it is built on something much older: Logic. Without the foundations laid by ancient philosophers, your favorite AI tools wouldn't exist today. Think about it. Every prompt you write is a logical request. Every algorithm is a sequence of "If-Then" statements. Every neural network is a web of mathematical reasoning. We often get distracted by the "magic" of technology. But the real magic is the human ability to reason. From Aristotle’s syllogisms to modern-day Large Language Models... The bridge is shorter than you think. 🌉 Today is World Logic Day. It’s a reminder that in a world full of noise, clear thinking is your greatest competitive advantage. Logic helps us: Deconstruct complex problems. 🧩 Avoid emotional biases in leadership. Build more ethical and reliable AI systems. As we race toward an AI-driven future, let’s not forget the "Logic" that started it all. "Cogito, ergo sum." — I think, therefore I am. Logic is the base for AI, but human reasoning is the heart of it. ❤️ How has logical thinking helped you solve a "tough" problem this week? Share your perspective below! 👇 #WorldLogicDay #AI #CriticalThinking #Innovation #FutureOfWork