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Anonymous

Hey I am on Medial • 3m

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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Rohit joshi

Dev dev dev • 4m

🚀 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

Saswata Kumar Dash

Buidling AlLiN|AI Re... • 26d

🚨 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

Aroneo

| Technologist | ML ... • 4m

In the ever-evolving AI landscape, a new player is making waves — Deepseek. While OpenAI, Google DeepMind, and Meta AI have been dominant forces, Deepseek is emerging as a formidable contender in the AI race.The recent buzz around Deepseek stems from

Yogesh Jamdade

..... • 4m

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

Account Deleted

Hey I am on Medial • 2m

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

Chetan Bhosale

Software Engineer | ... • 7m

💡 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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Amit Soni

A billion dollar dre... • 6m

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

Inactive

AprameyaAI • 11m

Meta has released Llama 3.1, the first frontier-level open source AI model, with features such as expanded context length to 128K, support for eight languages, and the introduction of Llama 3.1 405B. The model offers flexibility and control, enabli

Account Deleted

Hey I am on Medial • 1y

Unravel the mysteries of Large Language Models! Discover the top 10 essential terms every beginner should know. 🔑 Unlock the secrets of AI language processing and generation. 📚 Read now and stay ahead of the curve! 🚀 #LLMs #ArtificialIntelligen

Comet

#freelancer • 3m

If you're building AI agents, you should get familiar with these 3 common agent/workflow patterns. Let's break it down. 🔹 Reflection You give the agent an input. The agent then "reflects" on its output, and based on feedback, improves and refines.