RAG vs LLM: What’s the Difference and Why It Matters in AI? Today I explored one of the most important concepts in AI: LLM (Large Language Models) vs RAG (Retrieval-Augmented Generation). difference in simple words: LLM Uses only trained data to generate answers (limited to what it learned). RAG Uses trained data + external knowledge base (like documents, databases, or APIs) to fetch fresh info before generating output. In short: LLM = Brain RAG = Brain + Library This combination makes RAG more powerful, accurate, and useful for real-world applications like chatbots, enterprise search, and document assistants. #AI #MachineLearning #LLM #RAG #ArtificialIntelligence #TechLearning
LLM vs RAG: How AI's Brain and Library Differ
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One of the biggest challenges with Large Language Models (LLMs) is hallucinations. Hallucinations occur when AI tools confidently produce plausible-sounding answers that are factually incorrect. Why does this happen? And what can you do to reduce or prevent hallucinations? In this episode of our Data and AI Engineering in Five Minutes series, Shivam Chandarana (Technical Lead, Softwire) explains: ➡️ Why hallucinations happen ➡️ How Retrieval-Augmented Generation (RAG) improves LLM workflows ➡️ How RAG enables more personalised and reliable outputs #AI #LLM #RAG #ArtificialIntelligence #MachineLearning #DataEngineering
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45 Days AI Challenge: Day2 : Demystifying Large Language Models (LLMs)! 🧠 Ever wondered how AI understands and generates human-like text? It's all thanks to Large Language Models (LLMs)! 🔸 What are LLMs? An LLM is a powerful AI program designed to recognize, interpret, and generate text, among other complex tasks. They are trained on massive datasets, allowing them to grasp the nuances of human language. At their core, LLMs are built on the revolutionary transformer model. Simply put, an LLM is a sophisticated computer program that learns from vast examples to interpret human language or other intricate data. 🔸 How do LLM's work? ▶️ Tokenization: Text is broken down into smaller units (tokens). ▶️ Embedding: Tokens are converted into numerical vectors that represent their meaning. ▶️Transformer Mechanism: The model uses "attention" to focus on the most relevant parts of the input. ▶️Prediction: The model then predicts the next most probable tokens. ▶️Response Generation: It iteratively generates words, ensuring relevance and coherence in the final output. It's a fascinating blend of data, algorithms, and computational power that's reshaping how we interact with technology! #LLM #ArtificialIntelligence #AI #MachineLearning #DeepLearning #NaturalLanguageProcessing
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Is this the Next Big Leap for AI? Humans don't think in words—we think in ideas. While today's large language models (LLMs) are brilliant, they still operate token by token, often missing the forest for the trees. Think of it like preparing a presentation: we remember the key points, not the exact words. Each time we present, the wording changes, but the core message stays the same. That's the promise of Large Concept Models (LCMs). Instead of predicting the next word, LCMs aim to capture entire ideas as abstract, language-agnostic embeddings. This moves us closer to human-like reasoning and big-picture understanding. While still in their early stages, LCMs could be a massive shift—from processing language to understanding meaning. LLMs (Token-Based): [Start] → 'The' → 'cat' → 'sat' → 'on' → 'the' → 'mat' → [End] LCMs (Concept-Based): [Sentence] → [Concept Embedding] → [Generated Sentence] Do you think a focus on concept-level understanding could finally surpass today's LLMs? #LLMs #LCMs #ArtificialIntelligence #AI #MachineLearning
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#90th #LLMs Large Language models are the foundations of any AI based model. Any AI learns from #LLM. #LLM forms the data corpus Huge and large data needs to be fine tuned so that good and accurate results are ensured on the predecitive text. Any word or sentence is predicted with the help of #LLMs only. LLM is the foundational AI. #OnlineLearning #AI #ML #Data #AIModel #MLModel #Classifiers
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🤯 Fluent… but wrong? LLMs can spin convincing answers—even when they’re way off. New research reveals that large language models often generate “fluent nonsense” when reasoning outside their training data. Turns out, Chain-of-Thought prompting isn't a magic fix. ☁️ Good news for devs: This study offers a roadmap for testing and fine-tuning for better results. 🛠️ Full story here: https://lnkd.in/ep66sYkG #AI #LLM #MachineLearning #AIresearch Have you encountered “fluent nonsense” in your work with LLMs? Follow for the latest in AI breakthroughs and trends!
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Improve the accuracy of AI applications using Large Language Models (LLMs) by mastering data chunking strategies for Retrieval-Augmented Generation (RAG) systems. Discover essential chunking techniques, their trade-offs, and tips for optimizing RAG application performance. #RAG #LLM #chunking #AI #vectordatabase #retrievalaugmentedgeneration #this_post_was_generated_with_ai_assistance #responsibleai https://lnkd.in/eJT-G_cV
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I’ve been spending some time this week with Agentic Artificial Intelligence, and I just came across a concept that’s completely new to me: Large Reasoning Models (LRNs). Up to now, my understanding of AI has mostly been shaped by Large Language Models (LLMs) like GPT — systems built to recognize patterns and generate content. LRMs, on the other hand, are designed to reason and problem-solve, which feels like an entirely different step forward. From what I can tell, we’re still in the very early days of LRMs — maybe similar to where LLMs were five or six years ago. The potential is exciting: models that don’t just produce fluent text but can tackle complex, multi-step challenges. I’m also a bit skeptical. Moving from generating language to true reasoning raises big questions — about cost, accuracy, and how we’ll know if a model’s reasoning can be trusted. I’m learning as I go here, but it’s been eye-opening to think about what this next wave of AI could mean for problem-solving in the future. Has anyone else explored LRMs or LRNs? I’d love to hear what others are seeing or reading about in this space. #AI #GenAI #Learning #WeekendReading
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Making the Constitution easier to explore with AI ⚖️✨ Here’s a quick demo of the AI Legal Assistant I’ve been building 🎥 With just a simple query, the system retrieves insights from over 400 pages of the Constitution of India in natural language. 💡 Powered by: 🔹BGE-large-en-v1.5 embeddings 🔹LLaMA 3.1 8B responses 🔹Vector similarity search for accurate retrieval 🔹LibSQL for fast, scalable queries ⚡ Key outcomes: 🔹Reliable chunk-to-embedding mapping 🔹Sub-second response time Try it out here 👉 https://lnkd.in/gF6pzRbB #AI #LLM #Embeddings #VectorSearch #AIModels #GenerativeAI #LegalTech #Demo #OpenSource
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LLM (Large Language Model) : A machine learning model trained on massive language datasets to predict and generate human-like text based on patterns, mainly performing input-to-output transformations without intent or task execution. Generative AI : An AI category specialized in creating new content—text, images, code, or music—based on user prompts and learned patterns, but generally acts reactively and does not initiate actions on its own. AI Agents : Modular systems designed to detect intent, classify tasks, execute predefined rules or models, and interact with external tools or APIs to complete specific objectives, operating semi-autonomously within defined parameters. Agentic AI : Highly autonomous AI that sets and pursues its own goals, plans and reasons through complex sequences, orchestrates multiple tasks or sub-agents, adapts in real-time, makes decisions, and drives processes with minimal human oversight—proactive and outcome-oriented. #LLM #GenerativeAI #AIAgents #AgenticAI Follow me on LinkedIn : https://lnkd.in/gpTWYeyw
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🚨 Why do language models "hallucinate"? 🚨 Just read OpenAI’s latest whitepaper, and it’s essential for anyone working with generative AI! The heart of the issue: language models are trained and graded to always provide an answer, even when they don’t know the truth—leading to those infamous “hallucinations.” The paper argues that the root cause is NOT just data or algorithms, but how we incentivize models to guess instead of admit uncertainty. 🧠 Key insight: Hallucinations are encouraged when benchmarks penalize honesty (like saying “I don’t know”). Changing what we reward in evaluation is vital to building more trustworthy AI. Let’s advocate for new industry standards where uncertainty is respected, not punished. A small shift in evaluation could make a big difference in the reliability of AI systems! #AI #ai-for-all #LanguageModels #TrustworthyAI #OpenAI #Hallucinations #MachineLearning #Evaluation
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