I Built Self-Correcting AI That NEVER Hallucinates! (Corrective RAG Tutorial)

Description

Ever wondered how to build an AI system that can fact-check itself and NEVER give wrong answers? In this tutorial, I'll show you how to implement Corrective RAG (Retrieval-Augmented Generation) from scratch using LangChain, ChromaDB, and Groq's free LLM endpoints.​ https://www.linkedin.com/in/siddharth-kharche-9a0681217/ https://github.com/siddharth-Kharche/Day-2-Corrective-RAG 🔥 What You'll Learn: What is Corrective RAG and why it's better than traditional RAG How to build a self-correcting AI system that validates its own responses Step-by-step implementation with LangChain and ChromaDB Using Nomic embeddings and Groq's free open-source LLMs 5-step corrective process: retrieve, generate, critique, retrieve again, and improve Real-world example with Wikipedia data ⚙️ Tech Stack: LangChain Framework for RAG pipelines ChromaDB Vector Store for semantic search Groq API for free LLM access (Llama 3) Nomic-embed-text-v1.5 for embeddings HuggingFace models Python & Google Colab 📚 What is Corrective RAG? Corrective RAG takes traditional RAG to the next level by adding a self-correction layer. Instead of just retrieving documents and generating answers, it critiques its own responses, identifies gaps or errors, retrieves additional context, and generates improved answers.​​ 🎯 Perfect For: AI Engineers building production RAG systems ML Developers working on chatbots and Q&A systems Anyone wanting to reduce AI hallucinations Portfolio projects that impress recruiters 💡 Why This Matters: Traditional RAG systems can hallucinate or provide incomplete answers. Corrective RAG solves this by implementing a self-critique mechanism that ensures accuracy and relevance before giving you the final response.​​ 🔗 Free Resources: Google Colab Notebook (linked in comments) Groq API Key (free tier available) HuggingFace API Access All code and documentation 📊 Project Implementation Steps: Load and preprocess web data Create vector embeddings with Nomic Build ChromaDB vector store Implement initial retrieval (top 3 docs) Generate initial response Create self-critique mechanism Perform additional retrieval based on critique Generate final corrected response 🚀 This project is perfect for your AI portfolio and will help you stand out in interviews! 💬 Drop a comment if you want more advanced RAG tutorials like Agentic RAG, Multi-Query RAG, or RAG Fusion!​ corrective rag rag tutorial langchain tutorial retrieval augmented generation ai tutorial machine learning projects vector database chromadb tutorial groq api nomic embeddings python ai projects ai projects 2025 self correcting ai rag from scratch langchain rag ai hallucination llm tutorial artificial intelligence machine learning tutorial deep learning projects nlp tutorial ai portfolio projects huggingface tutorial python tutorial coding tutorial ai engineer ml engineer data science projects open source llm free ai tools generative ai llm applications semantic search vector embeddings ai development tech tutorial programming tutorial software engineering ai interview projects ml portfolio ⭐ Don't forget to star the GitHub repo and follow for more AI/ML content! **#ArtificialIntelligence #MachineLearning #RAG #LangChain #Python #AI #DeepLearning #NLP #VectorDatabase #ChromaDB #CorrectiveRAG #AITutorial #CodingTutorial #TechEducation #DataScience #AIProjects #MLOps #OpenSource #LLMGenerativeAI