What is Retrieval-Augmented Generation (RAG) and why does it matter?

🚀 𝐑𝐀𝐆 𝐄𝐱𝐩𝐥𝐚𝐢𝐧𝐞𝐝: 𝐓𝐡𝐞 𝐄𝐧𝐭𝐫𝐲-𝐋𝐞𝐯𝐞𝐥 𝐓𝐫𝐚𝐢𝐧𝐞𝐞 (𝐄𝐋𝐓) 𝐀𝐧𝐚𝐥𝐨𝐠𝐲 𝐟𝐨𝐫 𝐀𝐈 If you've spent any time working with AI, you know that sometimes those big, brilliant language models can be a little too general. They need an injection of current, real-world context to be truly useful. That's where 𝑹.𝑨.𝑮. (𝑹𝒆𝒕𝒓𝒊𝒆𝒗𝒂𝒍-𝑨𝒖𝒈𝒎𝒆𝒏𝒕𝒆𝒅 𝑮𝒆𝒏𝒆𝒓𝒂𝒕𝒊𝒐𝒏) comes in—it's the smart solution making modern AI relevant, reliable, and grounded in the facts you need. Let's break down this powerful technique using a simple, relatable analogy: 𝑻𝒉𝒆 𝑬𝒏𝒕𝒓𝒚-𝑳𝒆𝒗𝒆𝒍 𝑻𝒓𝒂𝒊𝒏𝒆𝒆 (𝑬𝑳𝑻). 1️⃣ 𝐑𝐞𝐭𝐫𝐢𝐞𝐯𝐚𝐥: 𝐅𝐞𝐭𝐜𝐡𝐢𝐧𝐠 𝐈𝐧𝐟𝐨𝐫𝐦𝐚𝐭𝐢𝐨𝐧 🔍 -The ELT (our initial model, trained on general knowledge) joins a company. - The ELT needs context on the work culture and latest technology. They will actively look for internal documentation and stay updated rather than being stagnant. 2️⃣ 𝐀𝐮𝐠𝐦𝐞𝐧𝐭𝐚𝐭𝐢𝐨𝐧: 𝐀𝐝𝐝𝐢𝐧𝐠 𝐂𝐨𝐧𝐭𝐞𝐱𝐭 & 𝐄𝐧𝐡𝐚𝐧𝐜𝐢𝐧𝐠 𝐊𝐧𝐨𝐰𝐥𝐞𝐝𝐠𝐞 ✨ - In today's age, Augment means to understand the context and use the additional information to enhance the knowledge one already has. - The ELT obtains the data and needs to 𝒖𝒏𝒅𝒆𝒓𝒔𝒕𝒂𝒏𝒅 𝒕𝒉𝒆 𝒄𝒐𝒏𝒕𝒆𝒙𝒕 and how it can be applied in their daily job. Just having the data is not important; putting it into use is equally important. 3️⃣ 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐨𝐧: 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐧𝐠 𝐒𝐞𝐧𝐬𝐢𝐛𝐥𝐞 𝐎𝐮𝐭𝐩𝐮𝐭𝐬 💡 - Once the data is fetched and context is understood, it's time to 𝒑𝒖𝒕 𝒕𝒉𝒆 𝒌𝒏𝒐𝒘𝒍𝒆𝒅𝒈𝒆 to use by applying it to their daily job and enhancing the current workflow with these gained ideas. This is Generation. 𝐓𝐡𝐞 𝐑𝐀𝐆 𝐌𝐨𝐝𝐞𝐥 𝐢𝐧 𝐀𝐜𝐭𝐢𝐨𝐧: Similarly, the RAG LLM model 𝒓𝒆𝒕𝒓𝒊𝒆𝒗𝒆𝒔 information from the most updated database, 𝒖𝒏𝒅𝒆𝒓𝒔𝒕𝒂𝒏𝒅𝒔 𝒕𝒉𝒆 𝒄𝒐𝒏𝒕𝒆𝒙𝒕 (augmentation), and 𝒕𝒉𝒆𝒏 𝒂𝒑𝒑𝒍𝒊𝒆𝒔 𝒊𝒕 𝒊𝒏 𝒈𝒆𝒏𝒆𝒓𝒂𝒕𝒊𝒏𝒈 𝒔𝒆𝒏𝒔𝒊𝒃𝒍𝒆 𝒐𝒖𝒕𝒑𝒖𝒕𝒔 based on the prompt. Hope this helps simplify a core component of modern AI architecture! Tagging great AI content creators Prabh Nair and Chidambaram Narayanan for spreading the word. #RAG #LLM #ArtificialIntelligence #GenerativeAI #TechExplained

What is RAG (Retrieval-Augmented Generation)? Curious how modern AI systems combine retrieval and generation to deliver smarter, more accurate responses? Let’s decode how RAG works 🔹 What is RAG? RAG enhances Large Language Models (LLMs) by combining real-time data retrieval with text generation — producing responses that are not only fluent but also grounded in up-to-date, factual information. Imagine asking an AI a question and it instantly pulls insights from the latest reports, PDFs, or knowledge bases — that’s RAG in action. 🔹 How It Works 1️⃣ Document Processing: Raw data — PDFs, videos, CSVs — are broken down into smaller, meaningful chunks. 2️⃣ Embedding: Each chunk is converted into a vector (embedding) that represents its meaning. 3️⃣ Retrieval: When a query comes in, it’s also embedded and compared against stored data to find the most relevant context. 4️⃣ Augmented Query: The retrieved information is added to the original query to enrich the prompt. 5️⃣ Generation: Finally, the LLM generates a context-aware answer — accurate, relevant, and grounded in your data. 🔹 Why It Matters RAG bridges the gap between static LLMs and dynamic knowledge — enabling real-world applications that are faster, more reliable, and enterprise-ready. It’s the foundation behind modern AI assistants, enterprise chatbots, and domain-specific copilots.

You may have heard the term “RAG” in AI conversations, but what does it actually mean? RAG, or Retrieval-Augmented Generation, lets AI look things up before answering, rather than relying only on what it memorised during training. This makes their outputs traceable, verifiable, and grounded in real data/facts. This is a crucial step in creating trust in what AI tells you, whether it’s for customer support, internal reports, or business insights. It is essentially a way to make AI more accountable and verifiable. Learn more and see practical examples here: https://lnkd.in/eMBGqk2J

#Technical_Post_1 #AI What if AI could think and write like humans anywhere in the text? That’s diffusion LLMs. Meet dLLM. dLLM is an open-source library. It helps you train, run, and evaluate diffusion language models. Unlike autoregressive LLMs, it does not generate text token by token. Instead, it uses an iterative denoising process. This process is much faster than autoregressive generation. Diffusion LLMs aim to match autoregressive models in performance. At the same time, they offer unique advantages. → They can reason bidirectionally. → They can generate text in any order. → They can also fill in missing text naturally. → You can even finetune BERT into a lightweight chatbot. This uses masked instruction tuning. It shows that encoder-only models can generate text. They do not need to switch to autoregressive architectures. ModernBERT-large-chat reaches 93% of Llama3-1B’s MMLU performance. It does this with 60% fewer parameters. The library provides ready-to-use training pipelines. These support LoRA, DeepSpeed, FSDP, and multi-node distributed training. It also includes unified generators. These simplify inference across different diffusion architectures, like LLaDA, Dream, and BERT. EditFlow extends diffusion models with edit operations. It can insert, delete, or substitute text. This allows flexible non-autoregressive generation. It also handles variable-length sequences. Its position-relative text manipulation works better than standard masking approaches. Diffusion LLMs are not yet as high-quality as autoregressive models. However, they have made a lot of progress this year. This makes it an exciting time to explore them. The repo includes neat examples. These examples act as step-by-step tutorials for beginners. ♻️ Follow me for more AI. 🎗️ Don't forget to check out the comments section for the repo link.

Building Herctually: An AI Research Agent written in GO Every week, I review competitors' articles and summarize them in a report. It’s repetitive and not exactly the favorite part of my week. I have been using a few new AI tools, such as Amp, Opencode, and I thought it would be nice to have a tool that could make things easier. So I built "Herctually," an AI research assistant that automates the boring parts. We’ll try to build Hertcually in Go, from model setup to giving it powers like web search, reading, and report writing. But first, let's align on what an agent is. According to Thorsten Bell, an agent is “an LLM with access to tools, giving it the ability to modify something outside the context window“. How I see it is that an agent is a large language model (LLM) that can take actions, not just talk. It can search, write, read, and execute. Think of it as a chatbot with hands. We’ll need: Go Access to a model (via OpenRouter, so we can easily test GPT-4, Claude, etc.) A use case — in our case, automating research. A few tools — to ex https://lnkd.in/gxnN_u8i

When AI doesn’t simulate the real world, the enterprise pays dearly. . Deloitte just learned that lesson the hard way — $440,000 hard. Deloitte was recently caught wrongly using AI in a report for the federal government. This report controversy is a striking reminder of how dangerous unchecked hallucination can be when enterprises rely solely on large language models (LLMs). The LLM generated report contained fabricated citations, a fake court quote, and references to imaginary research — a textbook example of an LLM generating syntactically coherent but semantically false information. Why? As OpenAI’s paper “Why Language Models Hallucinate” explains, LLMs don’t have grounded access to truth; they approximate plausibility, not reality. - LLMs are text-based, not real world-based — they learn from linguistic correlations rather than causal interactions.  - LLMs lack spatial-temporal representation which are important for simulation, planning and foresight.  - LLMs can generate many correct outputs and still fail unpredictably — reliability doesn’t come from repetition; it requires causally grounded reasoning over time. A single mistake can cost tens of thousands, if not millions, of dollars. That’s why enterprises can’t rely on LLMs for truth-critical tasks: they generate fluent language, not grounded logic. At Skyfall, we’re building the world’s first Enterprise World Model (EWM) — a part of a new cognitive architecture designed for state-of-the-art reasoning and predictive modeling within the enterprise. Unlike LLMs that operate on surface text and predict the next best text token, the EWM builds a simulation layer capable of rolling out several hypothetical futures before picking the one best suited for the task. #AI #WorldModel #enterprise #Deloitte #GenerativeAI #LLM #ReinforcementLearning

Retrieval-Augmented Generation (RAG) is a transformative architecture in large language models (LLMs), designed to bridge the gap between model knowledge and real-world data by retrieving external information and integrating it into generative outputs. What Is RAG? RAG enhances traditional LLMs by introducing a retrieval component into the generation process. When a user submits a prompt, the system first searches document repositories, databases, or knowledge bases to fetch relevant, up-to-date information. This retrieved data is then combined with the original prompt and passed to the LLM, which generates a response grounded in both its own learned patterns and the provided external context. Why RAG Matters: LLMs can produce impressive results, but they have limitations: factual inaccuracies, outdated training data, and lack of specialized, domain-specific knowledge. RAG addresses these by dynamically incorporating trusted and current information, ensuring more accurate and reliable outputs. For organizations, RAG unlocks advanced AI use cases, such as: - Knowledge management and search - Conversational AI for customer support - Domain-specific Q&A systems - Real-time analytics and summarization How RAG Works: 1. Prompt Input: The user submits a query. 2. Retrieval Step: The system searches external sources—structured or unstructured—for relevant chunks of information, often using semantic search and embeddings. 3. Reranking: Advanced models may reorder retrieved results by relevance before integrating them. 4. Augmented Generation: The relevant chunks are added to the prompt and sent to the LLM, which generates a final answer using both its knowledge and the amped-up context. RAG Architectures & Innovations: Modern RAG can involve naive pipelines, advanced hybrid searches (semantic, vector, keyword), or modular adaptive flows, depending on the complexity and performance requirements. Emerging frameworks leverage techniques like prompt rewriting, feedback loops, and iterative retrieval to maximize answer quality in knowledge-intensive domains. Practical Impact: RAG enables authoritative, source-grounded, and traceable outputs, overcoming the "hallucination" risk of standalone LLMs. It is a core foundation for next-generation enterprise search, automated report generation, research tools, and conversational interfaces where accuracy and transparency are mission-critical. RAG is rapidly shaping the future of trustworthy, auditable, and hyper-relevant AI, making LLMs truly useful for business and specialist domains alike. #GenAI #AI #RAG

Every week, new AI research quietly redraws the boundaries of what a company even is. We are seeing a structural shift, and the data is now everywhere. Ten papers in just the past months point in the same direction: - Governance is becoming autonomous Researchers in “Sentinel Agents for Secure and Trustworthy Agentic AI” show agents can self-regulate using internal compliance and audit layers. - Small models are outperforming giants. The “Small Language Models” studies (arXiv: 2506.02153 / 2505.19529) prove that smaller, specialized models now rival GPT-class systems on reasoning per watt. - Finance is moving toward real-time truth. Work on self-evolving agentic systems (arXiv: 2508.07407) demonstrates how continuous-learning agents synchronize ledgers and data streams, no more “month-end.” - Memory has become infrastructure. “TRiSM for Agentic AI” reframes memory as a first-class governance layer. Whoever controls long-term context controls decision quality. - Agents now negotiate and enforce logic. Contract enforcement in multi-agent trust frameworks is becoming code, not policy. - AI agent teams are starting to self-organize. “Synchronization Dynamics of Collaborative Multi-Agent Systems” shows emergent specialization, structure becomes an outcome, not a design. - Operational tools are readable as language. Small-model experiments in “Assessing SLMs for Code Generation” hint that spreadsheets, scripts, and workflows are merging into one semantic layer. - Synthetic markets are becoming the new lab. Multi-agent simulations now test strategies with virtual customers before launch, turning market research into systems engineering. - Logistics is being re-optimized through cooperation. Agentic routing models cut resource use by coordinating decisions rather than optimizing in isolation. - And the blueprint for the Agentic Enterprise is emerging. Across these studies, a clear pattern forms: Goals -> Context -> Agents -> Governance -> Outcome. Step back and it’s obvious: Every function, from finance to operations, is being reconstructed around autonomy, memory, and coordination. The result isn’t AI inside the business. It’s the business becoming AI-native. We’re moving from companies that use agents to companies that are agents. From systems that humans operate, to systems that self-organize around goals. The future enterprise won’t be defined by departments or job titles. It’ll be defined by flows of intelligence, where roles appear when needed and dissolve when done. This is the new operating architecture. It scales by logic, not labor. We are seeing a redefinition of what a corporation even is. This might be in the "lab" for now, but it's going to happen in a very near future in "the real world".

👻👽The Magic of “Mixture-of-Experts” (MoE) 🧙♂️🧙♀️ Ever wondered how new AI models like GPT-5, Grok 4, or Gemini 2.5 Pro are becoming so powerful without costing a fortune to run? Meet Mixture-of-Experts (MoE) — a game-changing technique that makes Large Language Models (LLMs) more efficient, scalable, and specialized. 🧠✨ 🔹 What Is Mixture-of-Experts (MoE)? Think of MoE like a team of specialists. Instead of asking every team member to work on every task (like older AI models did), the system only calls the right experts for the job. 🧩 How it works: The model has many “experts” — small specialized sub-models (math, code, writing, etc.) A “gating network” acts like a manager, picking the best experts for the task Only those experts are activated → saving compute, boosting performance 👉 Result: The model becomes faster, cheaper, and smarter at focused tasks. 🔹 Real-World Examples (as of 2025) ModelDeveloperMoE?NotesGPT-5OpenAI✅ SpeculatedMassive scale, likely dynamic routingGrok 4xAI✅ ConfirmedMulti-agent MoE, very efficientGemini 2.5 ProGoogle✅ ConfirmedDesigned for efficient scalingClaude 4Anthropic❌Probably dense (no MoE yet)DeepSeek-V3DeepSeek✅ Confirmed671B total, 37B active per token 🔹 Why It Matters ✅ Efficiency: Uses less compute → faster and greener AI ✅ Scalability: Add more experts without slowing down ✅ Specialization: Experts learn unique skills → better accuracy ⚠️ Challenges Routing can sometimes misfire (wrong expert chosen) Requires more memory to store all experts Harder to interpret why a certain expert was picked 💡 TL;DR Mixture-of-Experts = Specialized AI teamwork. Instead of using the whole brain every time, the model just activates the smartest neurons for the task at hand. Smarter use of compute = better performance for less cost. That’s the future of AI — intelligent specialization. 🌍💻 👉 Curious takeaway: The next time you hear about “GPT-5” or “Grok 4,” know that there might be hundreds of tiny experts behind the scenes — working together to make your AI conversations faster and sharper than ever. Asharib Ali | Naeem H. | Ameen Alam | Daniyal Nagori | Muhammad Qasim | #AI #MachineLearning #Innovation #MoE #LLM #ArtificialIntelligence #GPT5 #DeepLearning #TechExplained #FutureOfAI

Are your AI tools operating in silos? Businesses are realizing that large language models aren’t enough on their own, especially when data lives across disconnected systems. Learn how the Model Context Protocol (#MCP) could be the missing piece of the puzzle that lets #AI agents work alongside people, securely access tools, and drive automated, multistep workflows. https://ow.ly/C0Qz50XnwiI