Corrective RAG | Self-Improving Retrieval-Augmented Generation | Nidhi Chouhan

Описание: Welcome to another exciting session in the Agentic AI Hands-On Bootcamp with Nidhi Chouhan!

In this video, we dive deep into Corrective RAG (C-RAG) — an advanced form of Retrieval-Augmented Generation that focuses on error correction and feedback-driven refinement of generated responses.

💡 What You’ll Learn:
✅ What is Corrective RAG (C-RAG)
✅ Difference between Standard RAG and Corrective RAG
✅ How C-RAG uses feedback loops to refine incorrect or incomplete answers
✅ The workflow: Retrieve → Generate → Evaluate → Correct
✅ Integration of LLM evaluators and correction models
✅ Real-world applications — chatbots, summarizers, QA systems, and assistants

By the end of this session, you’ll understand how Corrective RAG enables LLMs to self-evaluate and self-improve, bringing intelligence and accuracy to real-world GenAI systems.

📘 Part of: Agentic AI Hands-On Bootcamp
📌 GitHub Repository (Code + Notes):
👉 https://github.com/dearnidhi/Agentic-...

📩 Connect with Me:
✉️ [email protected]

📸 Instagram: @codenidhi | @dear_nidhi
💼 LinkedIn: Nidhi Chouhan

✨ Don’t forget to LIKE 👍, SHARE 📢 & SUBSCRIBE 🔔 for more GenAI, LangGraph, and Agentic AI tutorials!


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