How to Understand Language Models

If you work with AI and still don’t truly understand how Transformers work, you’re flying blind. This interactive repo will change that. The Transformer Explainer is a free, web-based tool that lets you peek inside GPT-2’s brain, right in your browser. You can type a sentence, run it, and watch exactly how the model processes each token, layer by layer. Why it’s worth your time: 🔸Clear & visual. No overwhelming math, just intuitive, interactive diagrams. 🔸Hands-on learning. See attention flows, token relationships, and layer activations in real time. 🔸From basics to depth. Understand both the big picture and the fine-grained mechanics. Transformers are the backbone of modern LLMs. This tool pulls back the curtain so you can use them more effectively and understand their limits. Stop treating Transformers as magic. Spend an hour here and you’ll never look at AI the same way again. 🔗 Check it out → https://lnkd.in/dUdNDB37 #ai #genai #transformers

Fascinating visualisation of the Transformer architecture! It beautifully illustrates how the self-attention mechanism enables models to process entire sequences simultaneously, capturing long-range dependencies more effectively than previous architectures. For those interested in delving deeper, I highly recommend exploring the Transformer Explainer (https://lnkd.in/eTCgDUAE ), an interactive tool that allows you to experiment with a live GPT-2 model and observe how internal components work together to predict the next tokens.

🤯 Check this: TRANSFORMER EXPLAINER, an interactive visualization tool designed for non-experts to learn about Transformers through the GPT-2 model.

AI is evolving fast and it's powerful. But it’s being rushed and oversold, especially with the claim that it can replace people, which isn't true yet and it's making the industry look bad.

🚀 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

Most people think AI performance is about “how smart the model is.” In reality, the same AI can deliver wildly different results — all depending on how you ask. Zero-Shot, Few-Shot, and Chain-of-Thought prompting aren’t just formatting tricks; they’re powerful ways of activating the model’s reasoning. The problem? Even those who know the terms often default to just typing a quick question and hitting enter. 🚀 Two core techniques power most advanced prompting strategies: Few-Shot → Give the model examples so it can spot patterns. Chain-of-Thought → Show the reasoning process step-by-step, not just the answer. Everything from Self-Consistency to ReAct builds on these foundations. But here’s the catch: there’s no one-size-fits-all. You must balance model type, cost, speed, and accuracy for each use case. The real skill in AI isn’t “knowing the right answer” — it’s setting the stage so the AI can deliver its best answer. So… how much are you engineering your prompts, and how much are you leaving to chance? This post was created with the assistance of Generative AI. #LinkedInTips #ContentStrategy #AI #PromptEngineering #Storytelling #MarketingTips

RAG vs. Fine-Tuning: It's one of the most debated topics in AI. Here’s a simple breakdown of when to use each. Confused about whether to use RAG or Fine-Tuning for your AI project? Let's clear it up with a practical guide. 🔵 Use RAG (Retrieval-Augmented Generation) when you need to: Access real-time or frequently changing information. Eliminate model 'hallucinations' by grounding it in facts. Cite specific sources for its answers. Implement a solution quickly and cost-effectively. Think: Factual Knowledge & Accuracy. 🟠 Use Fine-Tuning when you need to: Teach the AI a very specific, nuanced style, tone, or format. Change the core behavior or 'personality' of the model. Embed specialized knowledge that is static and stylistic. Handle complex, domain-specific instructions. Think: Specialized Skills & Behavior. They aren't mutually exclusive, but knowing where to start is key to building effective and reliable AI. Did this clear things up for you? What other AI topics are you debating? Let's discuss in the comments! 👇 #AI #TechDebate #ArtificialIntelligence #RAGvsFineTuning #MachineLearning

Have you ever wondered why you always end up going back and forth with AI just to get something usable? Most people expect AI to just work like magic. But the truth is, when you dive in before knowing a few crucial steps thats what causes all that frustration That's why in this video, I'm going to show you: Why 90% of people struggle with AI and waste time going back and forth  What context engineering is and the 5 weird superpowers it gives AI And how to easily apply it so you're ahead of 90% of people using AI I know this works because I've built over 200 AI systems for different companies, tested every approach. So Let me explain what's actually happening when you’re in that back and forth with AI - because once you understand this, everything else will make sense. Watch this to find out 👉 Why Context Engineering gives AI Weird Superpowers https://lnkd.in/esHwXrHv

Retrieval-Augmented Generation (RAG) isn’t just another AI buzzword—it’s a game-changer for how we use large language models in real life. Instead of relying on static training data, RAG applications pull in live, trusted knowledge from external sources and combine it with generative AI. The result? 1. Answers grounded in facts, not hallucinations 2. Domain-specific expertise without retraining a model 3. Dynamic, up-to-date intelligence at your fingertips The beauty of RAG is that it bridges the gap between raw generative power and real-world accuracy. It lets organizations use AI responsibly—without handing over decision-making to a black box. We’re moving into a world where AI is only as good as the knowledge it can reach. RAG is how we get there. #Artificialintelligence #GenerativeAI #AIApplications #Innovation

🚨 Not Every Problem Needs Al In the rush to stay "cutting-edge," many organizations jump straight to Al and machine learning. Sometimes, simple human-written rules are more effective, they can be: -Transparent: you know exactly why a decision was made -Predictable: consistent behavior under known conditions -Cost-effective -Faster to implement Al is amazing for complex, ambiguous, or high-variability problems, but using it just to "stay modern" can add unnecessary complexity, cost, and risk. Use Al where it truly adds value, and rely on rules when they're sufficient. Remember, the smartest solution in the simplest one. #AI #MachineLearning #DecisionMaking #TechStrategy #Businessintelligence