ASHISH KUMAR’s Post

Midweek update at Outamation and what it has taught me: I solved for enhancing document query accuracy with efficient text processing. My Artifact: • I built a RAG pipeline to extract precise answers from financial PDFs • Tech stack: LlamaIndex + HuggingFace → accurate and context aware query responses Biggest insight: Using 512-token chunks with 50-token overlap improved response relevance by 30%. I processed a financial PDF to deliver high-accuracy answers to complex queries, streamlining information retrieval. This project taught me how small configuration tweaks, like optimizing chunk size, can drive big wins in AI performance. Shout-out to #Extern and #Outamation for the ride! #AI #LearningInPublic #ProductStrategy

Which approach between RAG and CAG would you pick for AI-powered generation?a When building intelligent systems, one of the biggest challenges is ensuring AI doesn’t just generate fluent answers, but reliable and contextually accurate responses. That’s where Retrieval-Augmented Generation (RAG) and Context-Augmented Generation (CAG) come in. Both methods share the same goal, to strengthen AI with external knowledge but they take very different routes: Here’s a quick overview : 🔶 RAG (Retrieval-Augmented Generation) 1. Works by retrieving external knowledge from databases or documents. 2. Pipeline includes tokenization, embedding generation, knowledge retrieval, and context selection. 3. Ensures relevance by ranking chunks and filtering noise before generation. 4. Validation focuses on accuracy checks and bias removal. 5. Best for: Dynamic knowledge retrieval where the database updates frequently. 🔷 CAG (Context-Augmented Generation) 1. Instead of retrieving, it injects context directly from a knowledge base or domain memory. 2. Uses context expansion to enhance responses. 3. Validation focuses on consistency checks and context alignment. 4. Ensures smoother integration of structured knowledge. 5. Best for: Stable, domain-specific use cases where context is predefined. Key Insight: - Use RAG when your system needs to pull in fresh, external knowledge. - Use CAG when you want deeper contextual alignment from predefined knowledge sources. Save this breakdown and feel free to share with others so they can learn how to quickly choose the right approach for the next AI project! #RAG #CAG #AI

I’ve said it before and I’ll say it again: RAG is the #1 use case for GenAI and it will be the backbone of AI agents. Why? Because RAG is the fastest way to unlock the power of your own data in GenAI use cases. You can deploy it from day one, and more importantly, it drastically reduces hallucinations. But here’s the thing: ➡️ Building a RAG pipeline is easy. ➡️ Engineering one that consistently works at scale is the real challenge. The future isn’t just retrieval. It’s about reasoning + advanced techniques layered on top. Below some of the most powerful thanks to Weaviate! 🔸Chain of Thought (CoT): make the model show its work. 🔸ReAct: reason → act → reason again. 🔸Tree of Thoughts: explore multiple reasoning paths in parallel. 🔸Query rewriting: smarter retrieval = better answers. The next frontier isn’t more hype. It’s RAG done right. Full ebook here 👉 https://lnkd.in/dY2VsYSX #ai #rag #agents

🔍 What is RAG, the RAG Model & RAG Agents? In today’s AI-driven world, one concept that’s creating real buzz is RAG (Retrieval-Augmented Generation). But what exactly does it mean, and why should businesses, developers, and professionals care? ✨ RAG (Retrieval-Augmented Generation) combines two powerful worlds: 1️⃣ Retrieval → Pulling in relevant, up-to-date information from trusted sources. 2️⃣ Generation → Using Large Language Models (LLMs) to create accurate, human-like responses. 💡 RAG Model → The framework that blends both retrieval & generation, ensuring responses are grounded in facts, not just guesses from an AI model’s training data. 🤖 RAG Agent → The practical application of RAG in action. Think of it as an intelligent assistant that doesn’t just “know,” but also “checks and verifies” before answering. This makes it powerful for: Customer support Knowledge management Research & content creation Business decision-making ✅ The real advantage of RAG? It bridges the gap between static AI knowledge and dynamic real-world information—bringing accuracy, trust, and context into every interaction. 🚀 In short, RAG is not just another buzzword. It’s a game-changer for how we use AI in daily business operations and future innovations. #RAG #RAGModel #RAGAgent #ArtificialIntelligence #AI #MachineLearning #GenerativeAI #LLM #KnowledgeManagement #AIForBusiness #FutureOfWork

While training #AI models and building #RAG pipelines, one lesson kept repeating itself -> 𝗰𝗼𝗻𝘁𝗲𝘅𝘁 𝗯𝗲𝗮𝘁𝘀 𝘀𝗰𝗮𝗹𝗲. I’ve been working on training AI models and experimenting with 𝗥𝗔𝗚 (𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹-𝗔𝘂𝗴𝗺𝗲𝗻𝘁𝗲𝗱 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻) 𝗽𝗶𝗽𝗲𝗹𝗶𝗻𝗲𝘀 lately, and a few things stood out. In one case, while building a knowledge assistant for structured documentation, we assumed that adding more data would improve accuracy. In practice, it did the opposite. Retrieval became noisy, latency went up, and responses lost context. What made the real difference was thoughtful system design: semantic chunking, hybrid retrieval (vector + keyword), domain-specific embeddings, and most importantly, human-led dataset curation. A smaller fine-tuned model with this setup consistently outperformed a larger #LLM without retrieval. Talking through latency vs. accuracy or whether fine-tuning is worth it with peers has revealed insights no benchmark ever could. For me, the key takeaway is simple: 𝗔𝗜 𝘀𝘆𝘀𝘁𝗲𝗺𝘀 𝗽𝗲𝗿𝗳𝗼𝗿𝗺 𝗯𝗲𝘀𝘁 𝘄𝗵𝗲𝗻 𝘁𝗲𝗰𝗵𝗻𝗶𝗰𝗮𝗹 𝗱𝗲𝘀𝗶𝗴𝗻 𝗮𝗻𝗱 𝗵𝘂𝗺𝗮𝗻 𝗷𝘂𝗱𝗴𝗺𝗲𝗻𝘁 𝘄𝗼𝗿𝗸 𝗵𝗮𝗻𝗱-𝗶𝗻-𝗵𝗮𝗻𝗱. #ModelTraining #AI #RAG #LLM #AIEngineering #KnowledgeManagement #TrainingResponses #AIBasics

🤖 𝐔𝐧𝐥𝐨𝐜𝐤 𝐭𝐡𝐞 𝐭𝐫𝐮𝐞 𝐩𝐨𝐰𝐞𝐫 𝐨𝐟 𝐆𝐏𝐓-𝟓 𝐰𝐢𝐭𝐡 𝐭𝐡𝐞 𝐫𝐢𝐠𝐡𝐭 𝐩𝐫𝐨𝐦𝐩𝐭𝐬. Most people use AI like a calculator—but to get the best results, you need to 𝐬𝐩𝐞𝐚𝐤 𝐢𝐭𝐬 𝐥𝐚𝐧𝐠𝐮𝐚𝐠𝐞. This guide breaks down how to customize GPT-5's thinking style based on what you're trying to achieve: 🔑 𝐇𝐢𝐠𝐡 𝐑𝐞𝐚𝐬𝐨𝐧𝐢𝐧𝐠 For deep analysis, strategy, or complex decisions: • Ask GPT-5 to explore multiple approaches, compare options, and make decisions. • Example: “Create a 1-page plan for [goal] with multiple scenarios.” 💡 𝐌𝐢𝐧𝐢𝐦𝐚𝐥 𝐑𝐞𝐚𝐬𝐨𝐧𝐢𝐧𝐠 For quick, clear answers with minimal detail: • Ask GPT-5 for summaries, lists, or short explanations. • Example: “Summarize the following in 3 bullet points.” 📏 𝐕𝐞𝐫𝐛𝐨𝐬𝐢𝐭𝐲 Control the answer length: • Short answers? Ask for it in a few words. • Medium or long? Specify with a clear prompt. • Example: “Explain the key points in 150 words.” 🧠 𝐂𝐡𝐚𝐢𝐧-𝐨𝐟-𝐓𝐡𝐨𝐮𝐠𝐡𝐭 (𝐂𝐨𝐓) For step-by-step reasoning: • Break problems into clear, actionable steps. • Example: “Solve this business problem step-by-step.” 🛠 𝐓𝐫𝐞𝐞-𝐨𝐟-𝐓𝐡𝐨𝐮𝐠𝐡𝐭 (𝐓𝐨𝐓) For comparing multiple options: • Explore several possibilities, then choose the best one. • Example: “Suggest 3 strategies for increasing retention.” 📊 Want to improve your GPT-5 prompts? Follow these tips: 𝟏. 𝐒𝐭𝐚𝐭𝐞 𝐭𝐡𝐞 𝐫𝐨𝐥𝐞 GPT-5 should play. 𝟐. 𝐆𝐢𝐯𝐞 𝐜𝐨𝐧𝐭𝐞𝐱𝐭—goal, audience, and constraints. 𝟑. 𝐒𝐩𝐞𝐜𝐢𝐟𝐲 𝐨𝐮𝐭𝐩𝐮𝐭 𝐟𝐨𝐫𝐦𝐚𝐭 (list, bullet points, steps). 𝟒. 𝐀𝐬𝐤 𝐟𝐨𝐫 𝐢𝐦𝐩𝐫𝐨𝐯𝐞𝐦𝐞𝐧𝐭𝐬 to refine responses. 💬 How are you currently using GPT-5? What do you wish it could do better? Visit yallo.co today. Follow us on LinkedIn: YALLO Group Credit: Charlie Hills #AI #GPT5 #MachineLearning #GenerativeAI #Productivity #Tech

🚀 Ready to turn raw data into real insights? 📊 Here’s how content understanding is transforming the game: 🔍 Extract structured insights from documents, images, videos & more 🧠 Enhance context with GPT optimization & prompt tuning 🛠️ Apply advanced preprocessing & enrichment techniques ✅ Boost accuracy with confidence scoring & grounding 📈 Scale smarter with AI-powered automation Let’s make data work for you, not the other way around. #ContentUnderstanding #AI #MachineLearning #DataProcessing #IntelligentAutomation Link to the full video in the comments section. Image Credit: Microsoft

🚀 RAG vs Agentic RAG – What’s the difference? 🔹 RAG (Retrieval-Augmented Generation): Helps retrieve and generate answers by pulling information from a vector database. 🔹 Agentic RAG: Goes a step further — not just retrieving but also reasoning, planning, summarizing, and generating structured outputs like reports with the help of specialized tools/assistants. 👉 In simple terms: RAG = Retrieve + Answer Agentic RAG = Retrieve + Think + Act + Summarize The future of AI is moving from just giving answers ➝ to becoming autonomous assistants that can plan, analyze, and deliver deeper insights. #AI #RAG #AgenticRAG #LLM #ArtificialIntelligence #GenerativeAI

🔍 LLMs are brilliant—but they often lie with confidence. ❌ LLMs are smart… but they hallucinate. ❌ They sound confident… but can be wrong. ❌ They know a lot… but often not what you need. That’s where Retrieval Augmented Generation (RAG) steps in. 🚀 Think of it this way: 👉 An LLM is like a very smart student. 👉 But without notes, it might bluff its answers. 👉 RAG hands the student the right textbook before answering. RAG has quietly evolved from a simple hack into a cornerstone of trustworthy AI — powering chatbots, research assistants, and enterprise systems that actually know what they’re talking about. In my latest blog, I break down this journey: 👉 From early LLM hallucinations 👉 To the birth of simple RAG 👉 To today’s advanced pipelines (query rewriting, re-ranking, multi-modal RAG & more) 💡 Curious how RAG is transforming the future of AI? Read here 👇 🔗Link in the comments. #AI #RAG #LLM #ArtificialIntelligence #MachineLearning #GenerativeAI

🚀 AI is learning to think like a detective, not just a search engine. We’ve all been there — asking AI a question that requires reasoning, only to get a shallow or incorrect answer. Traditional methods like Retrieval-Augmented Generation (RAG) or even hybrid search (text + knowledge graphs) help, but they often miss the deeper connections between ideas. That’s where ThinkOnGraph 2.0 (TOG2) comes in. Instead of loosely stitching together text and graphs, TOG2 introduces a tight coupling hybrid system — creating a powerful feedback loop: 🔹 The knowledge graph guides the search for documents 🔹 The documents refine and enrich the graph search 🔹 Together, they dig deeper — like a detective following clues until the case is solved 📊 The results are game-changing: 85.7% boost in GPT-3.5’s reasoning performance Smaller models (like LLaMA-3 8B) outperforming much larger ones Success on brand-new, complex datasets where other methods failed 💡 Why does this matter? Because this approach moves AI from just finding information → to truly understanding it. That means fewer hallucinations, more trustworthy answers, and the potential to solve complex problems in science, medicine, and beyond. Read full paper: https://lnkd.in/epXU_hRK #artificialintelligence #reasoning #rag #hybridsearch #thinkongraph #aiinnovation