RAG - Retrieval Augmented Generation Here’s how it works in practice Knowledge Sources → PDFs, Docs, Databases Embeddings → your data broken into chunks & vectorized Vector Database → stores everything for quick retrieval Retrieval → finds the most relevant context (top-k…
Sounds about right, Python; this RAG breakdown is pretty clear, but the devil's in the details, right?
Learn to build production grade RAG systems :readytensor.ai/agentic-ai-cer…
RAG is transforming how AI systems access knowledge. Vector databases make semantic search incredibly powerful. The key challenge is chunking strategy and embedding quality. Getting retrieval right is often more important than the LLM itself.
Nice breakdown—RAG really puts your data at the core, making results more reliable and relevant.
Clearly, RAG turns scattered data into actionable knowledge in seconds.
RAG = finally giving LLMs a way to remember things without retraining the entire model. it's basically wikipedia for neural networks
💙⚡ Nice breakdown! RAG is basically giving your AI super memory: it pulls the most relevant info from PDFs, docs, or databases using embeddings and vectors, then uses that context to generate accurate responses.
Rag is done Google launched a tool called "file search tool" with gemini api with free storage and supports almost all types of files.
why point out offline & online embedding ? all we need to care is embedding model in both case needs to be SAME.
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The main problem is how you index and recover the information without loosing context and so.
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