2 posts tagged with "Responsible AI"

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.

The AI Index Report 2025, released by Stanford's Human-Centered AI Institute, presents the most comprehensive analysis yet of global developments in artificial intelligence. With data spanning research, investment, policy, hardware, and public opinion, this report is a goldmine for policymakers, developers, and enthusiasts alike. Here's a detailed summary of the top insights shaping AI's present and future.

📈 Performance: AI Is Improving Faster Than Ever​

• Benchmark Gains: Models improved dramatically on complex benchmarks:
  • SWE-bench (coding): 4.4% → 71.7% accuracy in just one year.
  • GPQA (graduate-level QA): +48.9 percentage points.
  • MMMU (multi-modal): +18.8 percentage points.
• Video Generation: 2024 saw high-quality video generation breakthroughs with tools like OpenAI’s Sora and Meta’s Movie Gen.
• Smaller, Smarter Models: Models like Phi-3-mini (3.8B params) match the performance of earlier 500B+ models on benchmarks like MMLU.