How RAG model improved AI with verified facts and sources

Accuracy is the currency of trust in AI. Reading Suleiman Najims breakdown of how Perplexity AI built a $14B company on the back of RAG (Retrieval-Augmented Generation). This is not just a technical upgrade, it’s a trust upgrade. Instead of “plausible answers,” RAG delivers verifiable insights, with sources you can check. That’s very helpful for: ✅ CXOs making high-stakes decisions ✅ Legal teams needing documented precedents ✅ Customer support delivering instant, accurate resolutions ✅ Researchers citing trusted data At WebOsmotic, we see RAG not as the foundation for the next generation of AI-powered business systems. Because in business, it’s not just what AI says, it’s what it can prove. Credit to Suleiman Najim for sharing. #RAG #AITransformation #EnterpriseAI #WebOsmotic #AILeadership

Now. This post by Suleiman Najim really caught my eye. And such a game changer for counter fraud intelligence, due diligence and investigation. GPT search engine with source citation. Worth a read. https://lnkd.in/eUh9D3Df

#Perplexity AI and just ONE main improvement that differed from #ChatGPT: The #RAG model, with 3 core functions: #Retriever #Augmenter #Generator. #Retriever: searches databases and the internet for relevant information about your question. #Augmenter: analyzes the retrieved content and selects the most relevant answers, tracking the source of each fact. #Generator: creates a comprehensive response using those verified facts.

Perplexity AI LITERALLY built a $14 billion company with just ONE main improvement that differed from ChatGPT. The core problem RAG solves is AI hallucinations and outdated information. For example, if you ask a standard chatbot "What's the population of China in 2025?", it might give you a confident-sounding answer like "1.34 billion." But how do you know if that's accurate? It might be making it up or using old data from its last training session. With traditional AI, there's no way to verify the source, leaving you flying blind when making critical business decisions or conducting research. The RAG model changes everything by acting like an intelligent research assistant with three key functions: Retriever: First, it searches your specific databases or the internet to find relevant, real-time information related to your question. Augmenter: Next, it analyzes the retrieved content, selects the most accurate facts, and, most importantly, tracks the source of each piece of data. Generator: Finally, it uses those verified facts to create a comprehensive, well-structured response. The best part? It includes citations and sources, so you can see exactly where every piece of information came from. This gives AI the ability to finally say, "according to this source," instead of just pretending to know everything. Companies like Perplexity have turned this into a powerful search engine. Now, lawyers use it to find case precedents with proper documentation, students write research papers with accurate references, and customer support teams pull verified answers directly from company documentation. If you want to build your own RAG-powered AI assistant with real-time sources and citations, here's how: Comment "RAG" below, and I'll DM you a complete, step-by-step guide. - Like this post. - Make sure we're connected so I can send you the guide. - (Optional) Repost this to get the guide faster! Go check out our website for 4k free n8n agents - https://lnkd.in/g_tegp5W

Perplexity AI built a $14 billion company with just one significant improvement that differed from ChatGPT. Now that one update is being utilized by every industry. It's called the RAG model. Here's the problem they solved: If you ask ChatGPT, "What's the population of India in 2025?" and it confidently says 1.45 billion people, sounds good, right? But what if it just made that up or it's giving an answer from its last training date which might not be accurate now? With regular ChatGPT, there's no way to check the answer. It could be totally wrong and you'd never know. So when you're making business decisions or doing research, you're flying blind. RAG works like an intelligent research assistant with three core functions: Retriever, Augmenter, and Generator. First, the Retriever searches databases and the internet for relevant information about your question. Then the Augmenter analyzes the retrieved content and selects the most relevant answers, tracking the source of each fact. Finally, the Generator creates a comprehensive response using those verified facts. But here's the best part: It includes sources showing exactly where each piece of information came from. For example, if an investor asks about emerging market trends, traditional AI might generate plausible-sounding but unverified information. But with RAG, it searches financial databases, finds verified reports from Bloomberg or McKinsey, and provides analysis with proper citations. Perplexity turned this into a search engine that actually explains things. Now lawyers use it to find case precedents with documentation. Students write research papers with proper references and citation. Customer support pulls accurate answers from company documentation. Good thing is AI can finally say "according to this source" instead of just pretending to know everything.

I second ALL of that —especially in Research & the Sciences, where knowledge is always evolving. “””Good thing is AI can finally say "according to this source" instead of just pretending to know everything… “”” Verifiable, real-time retrieval with traceability removes the guesswork of what was—and keeps us focused and helps to confirm what actually is with proper source and review at the time of inquiry.

Perplexity AI LITERALLY built a $14 billion company with just ONE main improvement that differed from ChatGPT. The core problem RAG solves is AI hallucinations and outdated information. For example, if you ask a standard chatbot "What's the population of China in 2025?", it might give you a confident-sounding answer like "1.34 billion." But how do you know if that's accurate? It might be making it up or using old data from its last training session. With traditional AI, there's no way to verify the source, leaving you flying blind when making critical business decisions or conducting research. The RAG model changes everything by acting like an intelligent research assistant with three key functions: Retriever: First, it searches your specific databases or the internet to find relevant, real-time information related to your question. Augmenter: Next, it analyzes the retrieved content, selects the most accurate facts, and, most importantly, tracks the source of each piece of data. Generator: Finally, it uses those verified facts to create a comprehensive, well-structured response. The best part? It includes citations and sources, so you can see exactly where every piece of information came from. This gives AI the ability to finally say, "according to this source," instead of just pretending to know everything. Companies like Perplexity have turned this into a powerful search engine. Now, lawyers use it to find case precedents with proper documentation, students write research papers with accurate references, and customer support teams pull verified answers directly from company documentation. If you want to build your own RAG-powered AI assistant with real-time sources and citations, here's how: Comment "RAG" below, and I'll DM you a complete, step-by-step guide. - Like this post. - Make sure we're connected so I can send you the guide. - (Optional) Repost this to get the guide faster! Go check out our website for 4k free n8n agents - https://lnkd.in/gcpvYrEX

Your AI prompts are failing — not because of ChatGPT, but because you’re vague. Most people type in: “Write a LinkedIn post for me.” And then wonder why it sounds robotic. The truth? If you don’t give AI clear instructions, you’ll get generic, low-quality results. Here’s the framework top creators use to get powerful, accurate content from ChatGPT: Role: ↳ “You are a [type of expert].” Context: ↳ “I need this for [project or platform] to [goal].” Examples: ↳ “Here’s a post I like — match the tone and structure.” Style: ↳ “Use a tone that’s [e.g. friendly, punchy, persuasive].” Format: ↳ “Keep it under 150 words, use bullet points.” Example prompt: “You are a seasoned B2B content strategist. I’m writing a carousel post for LinkedIn to explain how to build authority online. Use a clear, confident tone, include 4 bullet points, and keep it under 180 words.” Clear input = better output. Stop blaming the tool. Learn how to use it properly. Credit to: Social Media Sensei on Pinterest ------ Master the language of AI. One prompt at a time. How to Talk to AI 

🤔 Confession: I used to think AI prompting was just fancy ChatGPT tricks Then I realised I was spending 3 hours writing PRDs that AI could help me draft in 30 minutes. As PMs, we're drowning in documentation, analysis, and stakeholder updates. Meanwhile, 78% of companies are already using AI to streamline these exact workflows. 💡 Here's what changed my perspective: Before: "Write me a feature spec" After: "Draft a PRD for mobile checkout optimisation targeting frustrated cart abandoners, considering our 2-week sprint constraints and iOS-first approach" The difference? Specificity wins every time. 🎯 My Go-To Prompt Structure: What you need (the task) Who it's for (the context) How it should sound (references/style) Test the output (evaluate) Refine until it's right (iterate) 🚨 The Reality Check: Yes, 93% of companies are rushing to adopt AI. But here's the kicker — 95% of AI pilots fail because teams treat it like magic instead of a tool that needs skill. 💭 What I've learned the hard way: ✅ AI is great for: First drafts, data synthesis, brainstorming variations ❌ AI struggles with: Strategic decisions, user empathy, stakeholder politics The sweet spot? Using prompts to handle the grunt work so we can focus on the problems only humans can solve. 🔥 Real talk for my fellow PMs: How are you actually using AI day-to-day? Not the LinkedIn-perfect version, the messy, trial-and-error reality? I'm curious about: • Your biggest AI wins (and fails) • Prompts that actually save you time • Where do you draw the line between AI help and human insight Let's get real about what's working and what isn't. Drop a comment with your most useful (or hilariously bad) AI prompt attempt! 👇