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Anonymous

Hey I am on Medial • 9m

Retrieval-Augmented Generation (RAG) is a GenAI framework that enhances large language models (LLMs) by incorporating information from external knowledge bases, improving accuracy, relevance, and reliability of generated responses. Here's a more detailed explanation: What it is: RAG combines the strengths of both retrieval-based and generative AI models. It allows LLMs to access and incorporate information from external sources, like databases, documents, or web pages, to supplement their pre-existing training data. How it works: Retrieval: An information retrieval system searches external knowledge bases for relevant information based on the user's query. Augmentation: The retrieved information is then integrated into the LLM's input, providing it with context and factual grounding. Generation: The LLM uses this augmented input to generate a more accurate, relevant, and informed response.

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Anonymous

Hey I am on Medial • 4m

Guys which AI model would be best to make a RAG (retrieval augmented generation) tech product ?

Rohit joshi

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🚀 New Video Alert! 🎉 We've just released a tutorial on building a Retrieval-Augmented Generation (RAG) application using Ollama and Microsoft's Phi-3 model. Key Points: Ollama:A platform that enables running large language models locally, enhanc

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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

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How RAG Gen AI Helps Businesses Retrieval-Augmented Generation (RAG) enhances AI by combining real-time data retrieval with generative models, making it highly effective for businesses. Key Benefits: Better Decision-Making – Provides real-time ins

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Turn ANY Website into LLM Knowledge in SECONDS 🤯 Tired of LLMs with limited knowledge? 🧠 This video shows you how to easily scrape any website and turn it into a powerful knowledge base for your own custom AI agents. 🤖 We'll explore: * Crawl for

HEMANT GHUGE

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Most people think of RAG (Retrieval-Augmented Generation) as a text-only thing. But when we apply it to images, it unlocks serious potential — especially in safety, retail, and surveillance. I recently explored Vision-RAG using Weaviate + LangChain

Chetan Bhosale

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💡 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

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Account Deleted

Hey I am on Medial • 8m

Hello Friends, I’ve been diving deep into Retrieval-Augmented Generation (RAG) and its evolved form — Agentic RAG — lately, and I’m genuinely curious about how others are thinking about it. I’d love to hear your ideas, interesting use cases, or eve

Rahul Agarwal

Founder | Agentic AI... • 3m

Simple breakdown of different elements in AI systems today. Extremely easy to understand. Here’s more: 𝗟𝗟𝗠𝘀 → Great at text generation and reasoning, but limited to training data. 𝗥𝗔𝗚 (𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹-𝗔𝘂𝗴𝗺𝗲𝗻𝘁𝗲𝗱 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼