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🚨 Everyone says "RAG is dead" — but I say: It’s just been badly implemented. I’ve worked on AI systems where Retrieval-Augmented Generation (RAG) either changed the game… or completely flopped. Here’s the hard truth 👇 --- 🤯 Most teams mess up

LLM Capabilities, t2i generation via inference and resonance inside vector DB of the model is what makes images as this possible. Via : Midjourney

I feel pride in announcing our Made in India, Real-Time Vector Database - SwarnDB. SwarnDB is a SarthiAI initiative towards an effort to create an end to end eco-system for the future of AI. We tested SwarnDB with 100K vector records of 1536 dime

🚀 Excited to share my latest YouTube video! demonstrate how to integrate Microsoft's Semantic Kernel with Qdrant, the open-source vector database, to build an intelligent question-answering system for Wikipedia pages citeturn0search3 In this tu

Which LLM Model is bettter in terms of performance?

💡 5 Things You Need to Master for learn for integrating AI into your project 1️⃣ Retrieval-Augmented Generation (RAG): Combine search with AI for precise and context-aware outputs. 2️⃣ Vector Databases: Learn how to store and query embeddings for e

Which one is at the top in terms of Coding ??

Which one is a good investment option in terms of return and risk.