What is RAG?

𝐌𝐨𝐬𝐭 𝐀𝐈 𝐦𝐨𝐝𝐞𝐥𝐬 𝐝𝐨𝐧’𝐭 𝐚𝐜𝐭𝐮𝐚𝐥𝐥𝐲 “𝐤𝐧𝐨𝐰” 𝐲𝐨𝐮𝐫 𝐝𝐚𝐭𝐚. They generate answers based on what they were trained on — which means they can be: • outdated • incorrect • or missing context 💡 𝐓𝐡𝐚𝐭’𝐬 𝐰𝐡𝐞𝐫𝐞 𝐑𝐀𝐆 (𝐑𝐞𝐭𝐫𝐢𝐞𝐯𝐚𝐥-𝐀𝐮𝐠𝐦𝐞𝐧𝐭𝐞𝐝 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐨𝐧) 𝐜𝐨𝐦𝐞𝐬 𝐢𝐧. 👉 Instead of answering directly, RAG works in 3 steps: Search → Find relevant information (docs, PDFs, databases) Retrieve → Pull the most useful pieces Generate → Answer using that information 🧠 Simple idea: Normal AI → guesses RAG → looks up info first, then answers 📌 Example: Ask: “𝘞𝘩𝘢𝘵’𝘴 𝘰𝘶𝘳 𝘤𝘰𝘮𝘱𝘢𝘯𝘺’𝘴 𝘭𝘦𝘢𝘷𝘦 𝘱𝘰𝘭𝘪𝘤𝘺?” Without RAG → generic answer ❌ With RAG → pulls actual company document ✅ 🚀 Why it matters: • More accurate answers • Uses real-time/private data • Reduces hallucinations • Powers AI agents & copilots 𝐈𝐟 𝐲𝐨𝐮’𝐫𝐞 𝐛𝐮𝐢𝐥𝐝𝐢𝐧𝐠 𝐀𝐈 𝐭𝐨𝐝𝐚𝐲, 𝐜𝐡𝐚𝐧𝐜𝐞𝐬 𝐚𝐫𝐞 𝐲𝐨𝐮’𝐫𝐞 𝐮𝐬𝐢𝐧𝐠 𝐑𝐀𝐆 — 𝐞𝐯𝐞𝐧 𝐢𝐟