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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

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

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

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

💡 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

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

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

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

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.

LLM Post-Training: A Deep Dive into Reasoning LLMs This survey paper provides an in-depth examination of post-training methodologies in Large Language Models (LLMs) focusing on improving reasoning capabilities. While LLMs achieve strong performance