RAG Responsibly: Building AI Systems That Are Ethical, Reliable, and Trustworthy
In my previous post, we walked through how to build a RAG (Retrieval-Augmented Generation) chatbot — connecting a powerful LLM to your business or domain data. But building a working AI system is just the beginning.
If you're planning to take your application into the real world, there's one critical layer you can’t skip: Responsible AI.
Let’s take a high-level look at the key components that make up a responsible RAG system — from prompt validation and safe retrieval to output evaluation and continuous feedback loops.
🤔 What Is Responsible AI (and How Is It Different from AI Governance)?
Responsible AI is all about behavior:
It ensures your AI system produces outputs that are accurate, relevant, safe, and free from bias or hallucinations.
In contrast, AI governance focuses on the organizational side:
Things like policy, compliance, and access control.
Both matter — but in this post, we’ll focus on how to build RAG applications that behave responsibly when interacting with users.