Videos de ArtificialIntelligence
Videos etiquetados con "ArtificialIntelligence"
ArtificialIntelligence 4 videos
Master RAG in 6 Minutes
# RAG Explained: How AI Retrieves the Right Information Learn **Retrieval-Augmented Generation (RAG)** explained in simple terms. In this beginner-friendly tutorial, you'll understand how RAG works, why **Large Language Models (LLMs)** use it, and how AI systems retrieve relevant information before generating accurate, up-to-date responses. RAG is one of the most important technologies behind modern AI applications. It enables AI assistants to answer questions using external knowledge, documents, databases, and the latest information instead of relying only on what they learned during training. ### In this video, you'll learn: * What is Retrieval-Augmented Generation (RAG)? * Why Large Language Models (LLMs) need RAG * How RAG retrieves relevant information before generating an answer * The complete RAG architecture and workflow explained step by step * Real-world examples of RAG applications * Benefits and limitations of RAG * RAG vs Fine-Tuning: Which approach should you use? Whether you're a student, developer, AI engineer, data scientist, or machine learning enthusiast, this tutorial will help you understand one of the core building blocks of modern AI systems. This video also covers important AI concepts including: * Large Language Models (LLMs) * Generative AI * AI Agents * Vector Databases * Embeddings * Semantic Search * Knowledge Retrieval * Context Augmentation * Prompt Engineering * AI Application Development If you found this video helpful, please Like, Subscribe, and Share it with others who are learning Artificial Intelligence and Machine Learning. Subscribe for more beginner-friendly AI tutorials covering: * AI Agents * Retrieval-Augmented Generation (RAG) * Large Language Models (LLMs) * Model Context Protocol (MCP) * Prompt Engineering * Vector Databases * AI Engineering * Python for AI * Machine Learning * Generative AI * Open Source AI * AI Tools and Tutorials #RAG #AI #LLM
Write Your First AI LLM Call with LangChain & Groq
Here's a YouTube description optimized for beginners, searchability, and engagement: 🚀 Welcome to LangChain for Beginners! In this first video of the series, you'll learn how to make your very first LLM call using LangChain and the Groq API. We'll start with the fundamentals of chat models, understand how messages work, create a ChatGroq model, send prompts to an LLM, and inspect the response returned by the model. By the end of this tutorial, you'll understand the core interaction pattern used throughout LangChain: Messages → Model → Response 📚 What You'll Learn: ✅ What a chat model is ✅ SystemMessage vs HumanMessage ✅ How to connect LangChain to Groq ✅ Creating your first ChatGroq model ✅ Using invoke() to call an LLM ✅ Understanding AIMessage responses ✅ Exploring response metadata and token usage 🔗 Code Covered: • Loading environment variables with python-dotenv • Creating a ChatGroq model • Building conversations with messages • Making your first LLM request • Reading model responses This video is part of the LangChain for Beginners series, where we'll gradually build toward prompt templates, chains, LCEL, embeddings, RAG applications, tools, and AI agents. ⏱️ Chapters 00:00 Introduction 00:45 How Chat Models Work 02:10 Loading Environment Variables 03:15 Creating a ChatGroq Model 05:20 Understanding Messages 07:40 Invoking the Model 09:15 Reading Responses 10:30 Response Metadata Explained 12:00 Recap 💡 If you found this video helpful, consider liking the video and subscribing for more AI Engineering, LangChain, RAG, Agentic AI, and LLM tutorials. #LangChain #LLM #AIEngineering #GenerativeAI #Python #Groq #AIAgents #MachineLearning #ArtificialIntelligence #RAG #PromptEngineering
How Modern AI Systems Actually Work: RAG, Tool Calling & LLMs
In this MizuFlow.ai Foundation of Finance episode, Sung Lee, CFA, CPA, CA, explains the core architectural principles behind modern enterprise AI systems. Many organizations focus exclusively on the Large Language Model itself. However, real business value comes from combining: • LLM reasoning • enterprise data • tool calling • APIs • Retrieval Augmented Generation (RAG) • structured outputs • workflow orchestration This session explores how these components work together to create intelligent systems capable of solving real-world business problems. The objective is to help finance, accounting, and technology professionals understand how enterprise AI moves beyond simple chat interfaces into fully integrated operational platforms. 🧠 What This Video Covers Enterprise AI Is More Than an LLM A common misconception is that AI equals the model. In reality: Model ≠ System The model provides: • reasoning • language understanding • planning • decision support The application provides: • actions • integrations • workflows • business execution Real enterprise AI requires both. Local Inference vs Cloud AI The session compares two primary deployment approaches. Local Inference Models run on: • local servers • private infrastructure • enterprise-controlled environments Advantages: ✅ Privacy ✅ Data sovereignty ✅ Lower long-term inference costs Challenges: ❌ Hardware requirements ❌ Maintenance complexity ❌ Potentially weaker models Cloud APIs Examples include: • OpenAI • Gemini • Anthropic Advantages: ✅ State-of-the-art models ✅ Rapid deployment ✅ Minimal infrastructure Challenges: ❌ Ongoing API costs ❌ Data governance considerations ❌ Third-party dependencies The Role of Tool Calling A major theme throughout the module is: Reasoning vs Action The LLM performs reasoning. The application performs actions. Examples include: • database queries • ERP updates • report generation • sending emails • running calculations Through tool calling, AI becomes capable of interacting with real-world systems. Retrieval Augmented Generation (RAG) Enterprise AI systems often require access to information that was never included during model training. RAG solves this challenge. Question ↓ Document Retrieval ↓ Relevant Context ↓ LLM Reasoning ↓ Answer This enables AI to work with: • accounting policies • contracts • financial statements • internal procedures • audit documentation while reducing hallucinations. Structured Outputs The module explains why enterprise systems require: Structured Outputs rather than unpredictable text. Examples include: • JSON • XML • predefined schemas This allows software systems to reliably process AI-generated outputs. Example: { "customer": "ABC Corporation", "risk_score": 8.4, "action_required": true } Structured outputs are essential for automation. Learned Weights & Inference Engines The session also clarifies key technical concepts. Learned Weights The knowledge stored inside the model. These represent billions of learned relationships developed during training. Inference Engine The runtime environment responsible for: • executing the model • generating responses • serving predictions The inference engine transforms static model weights into useful business outputs. Finance & Accounting Applications These architectural components support: Financial Reporting Agents • retrieve supporting schedules • generate commentary • draft disclosures AP Automation Systems • OCR extraction • vendor validation • workflow routing • ERP integration FP&A Platforms • scenario analysis • forecasting • variance explanations • executive reporting Enterprise Knowledge Systems • policy search • tax research • accounting guidance retrieval • regulatory interpretation 🚀 Why This Matters The future of AI is not: Question → Answer The future is: Question ↓ RAG ↓ Reasoning ↓ Tool Calling ↓ Business Action ↓ Human Review This is the foundation of modern enterprise intelligence systems. DISCLAIMER & LIABILITY NOTICE: The content in this video is for educational and informational purposes only. It does not constitute financial, accounting, tax, or legal advice. No Professional Relationship: Watching this video or interacting in the comments does not create a CPA-Client or fiduciary relationship between you and Sung Lee. Software & Tools: Any code, software, or tools mentioned (including https://www.google.com/search?q=Katchiflow.com) are provided "as-is" for demonstration and drafting purposes only. Outputs should not be relied upon for tax or statutory reporting without independent verification by a qualified professional.
AI vs Machine Learning vs Deep Learning Explained Simply 🤖 | Full Beginner Guide 2026
🚀 Want to understand the difference between Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL)? In this video, we explain everything in simple Indian English with real-world examples, interview concepts, career guidance, and industry use cases. Perfect for students, freshers, software engineers, and anyone starting their AI journey in 2026. 🔥 In This Video: ✔ What is Artificial Intelligence? ✔ What is Machine Learning? ✔ What is Deep Learning? ✔ AI vs ML vs DL Differences ✔ Real-world Applications ✔ AI Engineer Career Roadmap ✔ Skills Required in 2026 ✔ Salary & Job Opportunities ✔ Beginner-Friendly Explanation ✔ Interview Questions & Answers 💡 Whether you're preparing for placements, coding interviews, or starting a tech career, this video will give you a complete understanding of AI technologies shaping the future. 📌 Topics Covered: Artificial Intelligence Machine Learning Deep Learning Neural Networks Generative AI AI Careers Data Science AI Engineering ChatGPT & Modern AI Tools 🔥 Subscribe for more: AI Tutorials • Cloud Computing • Interview Preparation • Career Guidance • Tech Trends • Coding Roadmaps #AI,#MachineLearning,#DeepLearning,#ArtificialIntelligence,#AI2026,#MLEngineer,#AIEngineer,#DataScience,#ChatGPT,#GenerativeAI,#Python,#Coding,#Tech,#InterviewPreparation,#SoftwareEngineer,#NeuralNetworks,#CloudComputing,#FutureOfAI,#AITutorial,#MLTutorial,#DeepLearningTutorial,#Programming,#Students,#TechCareer,#AIJobs,#AIInterviewQuestions,#LearnAI,#CodingInterview,#AIForBeginners,#techeducation #AI,#MachineLearning,#DeepLearning,#ArtificialIntelligence,#AI2026,#ChatGPT,#OpenAI,#GenerativeAI,#AITools,#AIEngineer,#MachineLearningEngineer,#DeepLearningAI,#NeuralNetworks,#Python,#Coding,#Programmer,#SoftwareEngineer,#DataScience,#DataScientist,#BigData,#Tech,#Technology,#TechNews,#Innovation,#FutureTech,#FutureOfAI,#LearnAI,#AITutorial,#MLTutorial,#DeepLearningTutorial,#CodingTutorial,#Programming,#ComputerScience,#Developer,#FullStackDeveloper,#CloudComputing,#AWS,#Azure,#GoogleCloud,#DevOps,#CyberSecurity,#InterviewQuestions,#InterviewPreparation,#PlacementPreparation,#FreshersJobs,#EngineeringStudents,#CollegeStudents,#StudyMotivation,#CareerGrowth,#HighSalarySkills,#PassiveIncome,#OnlineLearning,#TechCareer,#AIJobs,#RemoteJobs,#Startup,#Business,#DigitalMarketing,#Productivity,#Automation,#AIApps,#ViralVideo,#Trending,#ExplorePage,#YouTubeGrowth,#YouTubeSEO,#ContentCreator,#Vlog,#HindiTech,#IndianYouTuber,#TechIndia,#LearnCoding,#CodeWithAI,#AITips,#SmartStudents,#Education,#SkillDevelopment,#FutureSkills,#NoCode,#100DaysOfCode,#Reels,#ViralReels,#TechReels,#Shorts,#YouTubeShorts,#TrendingNow,#ML,#DL,#LLM,#GPT4,#OpenAIChatGPT,#AICommunity,#ArtificialGeneralIntelligence,#AGI,#AIRevolution,#NextGenAI,#AIExplained,#TechExplained,#BTech,#EngineeringLife,#CampusPlacement,#JobReady,#SelfImprovement,#SuccessMindset,#MakeMoneyOnline,#StudentLife,#AIForStudents,#LearnMachineLearning,#DeepLearningProjects,#AIProjects,#PythonProjects,#CodingLife,#ProgrammersLife,#TechWorld,#DigitalFuture,#InternetOfThings,#Blockchain,#SaaS,#Entrepreneurship,#Freelancing,#RemoteWork,#TechChannel,#EducationChannel,#Knowledge,#Motivation,#CareerTips,#CloudEngineer,#DataEngineer,#SoftwareJobs,#StudyWithMe,#LearnWithMe,#ExamPreparation,#CodingMotivation,#InnovationTech,#AIContent,#AIUpdates,#TrendingTech,#FutureCareer