The right way to build authorized RAG chatbots
Secure LLM apps from day one. Learn how to design RAG pipelines with built-in authorization to prevent data leaks and simplify your AI stack.
The right way to build authorized RAG chatbots launched on July 10, 2025 with 8 votes, #2,947 of 9,223 launches that month and more than 75% of that year's launches. The site was still online in September 2026, 1 year on.
What it does
Retrieval Augmented Generation (RAG) pipelines are great for finding relevant information, but terrible at respecting permissions. A few weeks ago, I met an ML engineer who had built a RAG chatbot interface for his company’s engineering documents. The system worked well – so well that the HR organization wanted their own chatbot too. Like any engineer, he was keen on keeping the system as simple as possible. There was only one problem – HR’s knowledge base is full of sensitive documents. When it came to enforcing permissions on his RAG vector searches, he had no idea where to start. This crystallized a challenge I've been observing across many organizations: while RAG systems excel at…from osohq.com
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Hi HN, I built Bot The Builder to solve a recurring problem I kept facing while building chatbots for clients: every time, I had to rebuild the same RAG stack, embed the same knowledge base, and deploy from scratch—even for small use cases. So I created a tool that lets you build and deploy AI agents from your own data (PDFs, docs, URLs, text) in under 60 seconds — no code required. You can embed the agent in any website or access it via API. ### Why I built it: - Most “AI chatbot builders” are either locked down or require complex LangChain pipelines. - I needed something fast, modular, and…
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