What is RAG? (And why it’s awesome)
Welcome to one of the brains behind Calk! Let’s break down RAG — in plain English.
🧩 RAG stands for "Retrieval-Augmented Generation"
It sounds fancy, but here’s the simple idea:
Imagine you’re asking ChatGPT a question. It tries to answer from what it already knows.
Now imagine if, before answering, it could go look into your company’s Notion, Google Drive, or Slack, find the exact info it needs — and then answer using that real information.
That’s RAG. 🧠🔍✍️
🧠 Why do you need RAG?
Here’s the thing: AI models like GPT or Claude have a memory that doesn’t update. They were trained months ago — so they don’t know:
Your latest processes
Your current clients
What your team said in Slack last week
But your company moves fast. You write new docs, launch new features, and change how things work — sometimes every day.
RAG lets your AI stay in sync.
By connecting to your tools, it can:
Retrieve your real, live knowledge
Answer based on what’s true right now
Keep learning without retraining the model
So your AI doesn’t guess — it knows.
🛠️ How does RAG work?
You ask a question. → Like: “What’s our refund policy?”
Calk searches your connected knowledge. → It looks through Notion pages, Google Docs, uploaded files, etc.
It finds the most relevant info. → Like a sentence in your company handbook.
Then it generates an answer using that info. → So it feels like it really understands your business.
That’s Retrieval-Augmented Generation: It retrieves before it generates.
🔗 How Calk uses RAG
Every time your agent answers with internal knowledge, RAG is doing the work in the background.
You don’t have to configure anything fancy: Just connect your tools → your agents become faster, smarter, and always up to date.
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