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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?

AI apps don’t search text. They search meaning. If you work with LLMs, you’re already relying on vector databases whether you realize it or not. Here’s what’s really happening behind the scenes. First, you organize data into collections. Think of

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

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