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Don't learn AI Agents without Learning these Fundamentals

🧪AI Agents Labs for Free: https://kode.wiki/3Wh4DZ6

Learn everything about AI agents from scratch in this comprehensive tutorial. No prior knowledge required. We’ll take you from zero to building production-ready AI systems …
with hands-on labs.

🎯 What You’ll Learn:
• AI Fundamentals – LLMs, tokens, embeddings, and context windows
• LangChain – Simplify AI development with pre-built components
• Prompt Engineering – Zero-shot, few-shot, and chain-of-thought techniques
• Vector Databases – Semantic search with ChromaDB and Pinecone
• RAG (Retrieval Augmented Generation) – Build intelligent document search
• LangGraph – Create multi-step AI workflows and agents
• MCP (Model Context Protocol) – Connect AI to external tools

🔧 Hands-On Labs Include:
✓ Making your first OpenAI API calls
✓ Building semantic search engines
✓ Creating RAG systems for document retrieval
✓ Developing multi-agent workflows
✓ Integrating external tools with MCP

Perfect for developers, data scientists, and anyone wanting to understand modern AI development. Follow along with free labs and build a real-world AI assistant that searches 500GB of documents in under 30 seconds.

🚨Start Your AI Journey with KodeKloud: https://kode.wiki/4qsrspX

⏰ TIMESTAMPS:
00:00 – Introduction to AI Agents
00:40 – How LLMs work in real time?
04:56 – Embeddings & Vector Representations
05:56 – How LangChain works?
10:12 – Practice Labs – Your First AI API Call
14:57 – Practice Labs – LangChain
17:57 – Prompt Engineering Techniques
21:21 – Practice Labs – Master Prompt Engineering
24:46 – Vector Databases Deep Dive
31:27 – Practice Labs – Build Semantic Search Engine
35:15 – RAG (Retrieval Augmented Generation)
38:14 – Practice Labs – RAG Implementation
42:14 – LangGraph for AI Workflows
45:51 – Practice Labs – Build Stateful AI Workflow
48:51 – Model Context Protocol (MCP)
51:56 – Practice Labs – Advanced MCP Concepts
55:21 – Conclusion

🔔 Subscribe to KodeKloud for more AI development tools and tutorials!

#AiAgents #AI #Aifundamentals #LangChain #MCP #LLMs #RAG #Langgraph #vectordb #promptengineering #VectorDatabases #Tutorial #kodekloud
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Don't learn AI Agents without Learning these Fundamentals
Now Playing
Don't learn AI Agents without Learning these Fundamentals
🧪AI Agents Labs for Free: https://kode.wiki/3Wh4DZ6 Learn everything …
🧪AI Agents Labs for Free: https://kode.wiki/3Wh4DZ6

Learn everything about AI agents from scratch in this comprehensive tutorial. No prior knowledge required. We’ll take you from zero to building production-ready AI systems …
with hands-on labs.

🎯 What You’ll Learn:
• AI Fundamentals – LLMs, tokens, embeddings, and context windows
• LangChain – Simplify AI development with pre-built components
• Prompt Engineering – Zero-shot, few-shot, and chain-of-thought techniques
• Vector Databases – Semantic search with ChromaDB and Pinecone
• RAG (Retrieval Augmented Generation) – Build intelligent document search
• LangGraph – Create multi-step AI workflows and agents
• MCP (Model Context Protocol) – Connect AI to external tools

🔧 Hands-On Labs Include:
✓ Making your first OpenAI API calls
✓ Building semantic search engines
✓ Creating RAG systems for document retrieval
✓ Developing multi-agent workflows
✓ Integrating external tools with MCP

Perfect for developers, data scientists, and anyone wanting to understand modern AI development. Follow along with free labs and build a real-world AI assistant that searches 500GB of documents in under 30 seconds.

🚨Start Your AI Journey with KodeKloud: https://kode.wiki/4qsrspX

⏰ TIMESTAMPS:
00:00 – Introduction to AI Agents
00:40 – How LLMs work in real time?
04:56 – Embeddings & Vector Representations
05:56 – How LangChain works?
10:12 – Practice Labs – Your First AI API Call
14:57 – Practice Labs – LangChain
17:57 – Prompt Engineering Techniques
21:21 – Practice Labs – Master Prompt Engineering
24:46 – Vector Databases Deep Dive
31:27 – Practice Labs – Build Semantic Search Engine
35:15 – RAG (Retrieval Augmented Generation)
38:14 – Practice Labs – RAG Implementation
42:14 – LangGraph for AI Workflows
45:51 – Practice Labs – Build Stateful AI Workflow
48:51 – Model Context Protocol (MCP)
51:56 – Practice Labs – Advanced MCP Concepts
55:21 – Conclusion

🔔 Subscribe to KodeKloud for more AI development tools and tutorials!

#AiAgents #AI #Aifundamentals #LangChain #MCP #LLMs #RAG #Langgraph #vectordb #promptengineering #VectorDatabases #Tutorial #kodekloud
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AI & Machine Learning Fundamentals: A Must-Know Guide for Beginners!
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AI & Machine Learning Fundamentals: A Must-Know Guide for Beginners!
Welcome to the ultimate guide on AI and Machine Learning! In this …
Welcome to the ultimate guide on AI and Machine Learning! In this video, we’ll break down the fundamentals of AI and ML, the differences between rule-based systems, machine learning, and …deep learning, and why AI-powered applications are transforming industries like healthcare, finance, and retail.

From AI chatbots to fraud detection and image recognition, learn how AI & ML are revolutionizing technology and what it means for YOU. Whether you’re preparing for an AWS AI certification or just curious about the future of AI, this video is the perfect place to start!

🚀Explore Our Top Courses & Special Offers: https://kode.wiki/3CzuOnc


📌 Topics Covered:
✔️ What is Artificial Intelligence (AI)?
✔️ The Role of Machine Learning & Deep Learning
✔️ How AI Improves Decision-Making & Efficiency
✔️ Real-World Applications of AI & ML
✔️ AI in Business, Healthcare, & Finance

⬇️Below are the topics we are going to discuss in video:
00:00 – Why Artificial Intelligence (AI) matters?
02:14 – Ai vs Rule-Based Systems
03:37 – What is Artificial Intelligence?
04:52 – Narrow AI vs General AI
06:20 – What is Machine Learning?
08:24 – Deep Learning & Neural Networks
10:16 – Comparing AI, Machine Learning and Deep Learning
15:56 – What is Inferencing in AI?
18:18 – Real-Time vs. Batch Inferencing
22:11 – Types of Machine Learning
24:21 – Supervised Learning
25:14 – Unsupervised Learning
26:26 – Reinforcement Learning

🔔 Subscribe for more tech tutorials and AI insights!

#AI #MachineLearning #DeepLearning #ArtificialIntelligence #AITutorial #ML #DataScience #TechEducation #AWS #SageMaker #Rekognition #TechExplained #AIForBeginners #LearnAI #TechTrends #FutureTech #AIApplications #NeuralNetworks #SupervisedLearning #UnsupervisedLearning #ReinforcementLearning #AIBasics #TechGuide #AIExplained #MLModels #AIInBusiness #TechTips #AIInnovation #AIRevolution #SmartTech #DigitalTransformation #AIResearch #TechSimplified #AIAndML #TechForAll #AICommunity #TechLovers #AIExperts #TechUpdates #AIForEveryone

For more updates on courses and tips, follow us on:
🌐 Website: https://kodekloud.com/
🌐 LinkedIn: https://www.linkedin.com/company/kodekloud/
🌐 Twitter: https://twitter.com/KodeKloudHQ
🌐 Facebook: https://www.facebook.com/KodeKloudHQ
🌐 Instagram: https://www.instagram.com/kodekloud/
🌐 Blog: https://kodekloud.com/blog/
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MCP Tutorial: Build Your First MCP Server and Client from Scratch (Free Labs)
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MCP Tutorial: Build Your First MCP Server and Client from Scratch (Free Labs)
🧪MCP Labs for Free: https://kode.wiki/4lFwf5p Ever wondered how AI …
🧪MCP Labs for Free: https://kode.wiki/4lFwf5p

Ever wondered how AI agents can book flights, check databases, or interact with websites? The secret is Model Context Protocol (MCP)!

In this hands-on MCP tutorial, …
we will show you exactly how to build your first MCP Server that can connect to ANY third-party service. No fluff, just practical coding examples you can follow along with.

What makes this video different? You get a real lab environment to practice everything we cover!

🧪MCP Labs for Free: https://kode.wiki/4lFwf5p

⚡ Quick Overview:
• MCP Basics in Simple Terms
• MCP Use Cases and Architecture
• Learn to use an Existing MCP Server
• Build Your first MCP Server

Perfect for beginners – no prior MCP knowledge needed!

⏰ TIMESTAMPS:
00:00 – Introduction to Model Context Protocol
00:42 – Why We Need MCPs
02:10 – Understanding AI Agents
07:54 – MCP Architecture Explained
08:37 – MCP Use Cases
10:30 – Lab Environment Setup
13:12 – What is Model Context Protocol (MCP)? Components Breakdown
14:37- MCP Architecture Explained
17:53 – Protocol – JSON-RPC(2.0)
19:50 – How to Use an Existing MCP Server? – Demo
26:44 – Building an MCP Server – Demo
35:52 – Building MCP Clients

🚨Check out our learning paths at KodeKloud to get started: https://kode.wiki/41NLyks

#MCPtutorial #mcp #ModelContextProtocol #AIAgents #Agenticai#buildmcpserver #API #Integration #Automation #kodekloud
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LangChain vs LangGraph: Which One Should You Use?
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LangChain vs LangGraph: Which One Should You Use?
🧪Try LangChain Labs for Free: https://kode.wiki/462mo31 🧪Try LangGraph …
🧪Try LangChain Labs for Free: https://kode.wiki/462mo31
🧪Try LangGraph Labs for Free – https://kode.wiki/41WTH62

Master AI agent development from scratch with this beginner-friendly LangChain and LangGraph tutorial. Build your first intelligent chatbot with …
memory, retrieval, and workflow automation using Python.

In this video you’ll master both LangChain and LangGraph, understanding exactly when to use each framework. We start with LangChain fundamentals for building chatbots with memory and RAG, then level up to LangGraph for complex, stateful workflows like research assistants that can loop, branch, and adapt based on conditions.

🎓 Perfect for Beginners – No AI Experience Required

⏰ Video Timeline:
00:00 – Why you need LangChain
00:58 – LLMs vs AI Agents Explained
02:05 – Traditional Software vs Agentic Software
02:29 – LangChain Core Components
03:36 – Traditional Software vs Agentic Software
04:47 – Practical Lab Demo Introduction
05:20 – Demo – LangChain
12:03 – Introduction to LangGraph
12:23 – LangChain vs LangGraph: What’s the real difference?
13:22 – Deep Research Assistant Use Case Example
13:56 – Traditional approach pain points
14:24 – Orchestration in LangGraph
15:11 – What is StateGraph?
15:42 – LangGraph Workflow
16:45 – LangGraph Adoption in Business Requirements
17:19 – Demo – LangGraph
24:55 – Conclusion & Free Lab Access

🚀 Hands-On Labs Included:
Follow along with our free interactive labs covering everything from installation to deploying production-ready AI agents. Build a customer service chatbot with LangChain, then create a sophisticated research assistant using LangGraph’s state management and conditional branching.

🧪Try LangChain Labs for Free: https://kode.wiki/462mo31
🧪Try LangGraph Labs for Free – https://kode.wiki/41WTH62


🔔 SUBSCRIBE for cutting-edge AI tutorials that actually matter!


#LangChain #LangGraph #Chatbot #AIAgent #LearnAI #Coding #python #kodekloud
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RAG Crash Course for Beginners
Now Playing
RAG Crash Course for Beginners
🧪RAG Labs for Free: https://kode.wiki/3KfeX1a Ever wondered how …
🧪RAG Labs for Free: https://kode.wiki/3KfeX1a

Ever wondered how ChatGPT remembers your documents or how AI searches through company data? The secret is RAG (Retrieval Augmented Generation)!

In this hands-on RAG tutorial, we …
will show you exactly how to build production-ready RAG systems from scratch. No fluff, just practical coding examples you can follow along with.

What makes this video different? You get a real lab environment to practice everything we cover!

🧪RAG Labs for Free: https://kode.wiki/3KfeX1a

⚡ Quick Overview:
• RAG Components Overview
• Vector Search & Embedding Models
• ChromaDB and VectorDB
• Document Chunking Strategies
• Complete RAG Pipeline Build

🚨Start Your AI Journey with KodeKloud: https://kode.wiki/41NLyks

⏰ TIMESTAMPS:
00:00 – Introduction to RAG Tutorial
01:15 – Simplest RAG Explanation
03:32 – When not to RAG?
07:40 – What is RAG?
11:49 – Free Lab 1: Keyword Search (TF-IDF & BM25)
15:02 – What are Semantic Search?
16:54 – Understanding Embedding Models
19:00 – Embeddings and Vectors
21:00 – The Dot Product
26:00 – Lab 2: Embedding Models
29:50 – Vector Databases Explained
33:04 – ChromaDB Tutorial
34:45 – Lab 3: Vector Databases
38:17 – Chunking Explained
39:39 – Document Chunking Strategies
43:22 – Lab 4: Document Chunking
48:45 – Build your RAG Architecture
49:31 – Lab 5: Complete RAG Pipeline
51:50 – Caching, Monitoring and Error Handling
56:34 – RAG in Production
58:08 – Conclusion


#RAG #RetrievalAugmentedGeneration #Vectordb #AI #EmbeddingModels #VectorDatabase #ChromaDB #AITutorial #SemanticSearch #LLM #OpenAI #DocumentChunking
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OpenAI Agent Builder for Beginners | Agent Builder vs n8n | KodeKloud
Now Playing
OpenAI Agent Builder for Beginners | Agent Builder vs n8n | KodeKloud
OpenAI released their brand-new Agent Builder, and it’s being called …
OpenAI released their brand-new Agent Builder, and it’s being called the N8N killer! But is it really that good? In this walkthrough, we dive deep into everything you can and …can’t do with it right now, from buggy MCP tools to powerful file search and chat agents. If you’re serious about building your own AI automations, watch this before switching from N8N!

What You’ll Learn:
✅How Agent Builder compares to n8n
✅Setting up chat workflows with Agent Builder
✅Pros, cons, and real-world limitations of Agent Builder

If you’re into AI automation, DevOps, or workflow orchestration, hit like and subscribe to our KodeKloud channel.

🚨Check out our complete n8n course on KodeKloud: https://kode.wiki/4qaLIwI

⏱️ Timestamps:
00:00 – Introduction to OpenAI Agent Builder?
00:37 – Agent Builder Interface Overview
02:27 – Agent Builder MCP Tool
04:51 – Agent Builder File Search Tool
06:38 – Creating Customer Support Chatbot
08:38 – Guardrail Node in Agent Builder
09:05 – Agent Builder vs N8N – Comparison
12:47 – Conclusion

🔔 Subscribe for more no-code AI tutorials!

#OpenAI #AgentBuilder #n8n #Automation #AIWorkflows #NoCode #AIChatbots #MachineLearning #OpenAI2025 #AIAgents #WorkflowAutomation #MCP #KodeKloud #ChatGPT5 #AIAutomation
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How does a Vector Database work?
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How does a Vector Database work?
🧪Try Vector Database Hands-On Labs for Free – …
🧪Try Vector Database Hands-On Labs for Free – https://kode.wiki/46vlLjG

Learn how Vector Databases are revolutionizing AI search and powering the next generation of intelligent applications!

In this comprehensive video, we’ll show you …
exactly how vector databases transform traditional keyword-based search into semantic understanding that actually comprehends meaning, not just matching words.

Ready to build your own vector database system? Access our FREE interactive labs where you can experiment with real vector implementations, test different embedding strategies, and see semantic search in action!

🧪Try Vector Database Hands-On Labs for Free – https://kode.wiki/46vlLjG

🎯 What You’ll Learn:
• What are vector databases and why they’re essential for modern AI
• How embeddings transform text into searchable numerical representations
• The magic behind 384-dimensional semantic space
• Vector similarity scoring and threshold optimization
• Critical chunking strategies for optimal retrieval
• Real ChromaDB implementation walkthrough
• Why traditional SQL databases fail with natural language queries

Perfect for developers, AI engineers, and anyone building intelligent search systems!

⏰ Timestamps:
00:00 – The Problem with Traditional SQL Search
00:49 – How does a Vector Database work?
02:10 – What Are Embeddings?
03:08 – Understanding Vector Dimensions
03:50 – Scoring in Vector DB
05:05 – Chunk Overlap in Vector DB
06:02 – Lab Demo – Setting up the Environment
06:39 – Lab Demo – Problem in Traditional SQL Database
07:13 – Lab Demo – Embeddings
08:23 – Lab Demo – Similarity Search
09:13 – Lab Demo – ChromaDB
10:10 – Conclusion & Free Lab Access

#VectorDatabase #AI #Embeddings #SemanticSearch #ChromaDB #Pinecone #ArtificialIntelligence #AITutorial #VectorEmbeddings #kodekloud
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MCP in 10 Minutes (Model Context Protocol + Free Lab)
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MCP in 10 Minutes (Model Context Protocol + Free Lab)
🧪MCP Labs for Free: https://kode.wiki/4nkTvFD Confused about Model …
🧪MCP Labs for Free: https://kode.wiki/4nkTvFD

Confused about Model Context Protocol? This beginner-friendly video explains MCP in simple terms + includes a FREE hands-on lab!

No experience needed! This video breaks down MCP …
(Model Context Protocol) into easy-to-understand concepts with real examples. You’ll discover how AI agents like Claude connect to external tools and databases, and why MCP is a game-changer.​

What’s Inside:
🎯 Simple MCP explanation (no jargon!)
🎯 Why MCP matters for AI applications
🎯 Step-by-step tutorial building a real MCP server
🎯 Free practice lab with flight booking example

Before MCP, connecting AI to external systems required custom code for every integration and a nightmare! MCP standardizes this process, letting you write the integration once and reuse it everywhere. Think of it like USB-C for AI connections.

🔧 Hands-On Labs Included:
The tutorial demonstrates solving the N*N integration problem through standardized protocol design. You’ll implement a complete flight booking MCP server featuring airport data resources, flight search tools, booking creation, and AI prompt templates for optimal interaction patterns.

🧪MCP Labs for Free: https://kode.wiki/4nkTvFD

⏰ TIMESTAMPS:
00:00 – Introduction to MCP
00:17 – The N*N Problem Explained
01:09 – How MCP Works
04:19 – MCP Server Architecture
05:19 – Hands-On Lab Overview
07:42 – Building Flight Booking Server
10:28 – Testing with Roo Code

Perfect for developers, AI enthusiasts, and anyone interested in building practical AI applications with external integrations.

🚨Start Your AI Journey with KodeKloud: https://kode.wiki/41NLyks

🔔 Subscribe to KodeKloud for more AI development tools and tutorials!

#MCP #ModelContextProtocol #Python #AIAgents #AnthropicClaude #LLM #APIIntegration #AITools #kodekloud
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Build an Email AI Agent in 10 Minutes | n8n Tutorial for Beginners | Free Labs
Now Playing
Build an Email AI Agent in 10 Minutes | n8n Tutorial for Beginners | Free Labs
🧪 Try n8n AI Agent Labs for Free – https://kode.wiki/4ohpTd9 Learn how …
🧪 Try n8n AI Agent Labs for Free – https://kode.wiki/4ohpTd9

Learn how n8n transforms workflow automation into powerful AI agents with visual drag-and-drop nodes, LLM integration, and production-ready email automation!

In this …
comprehensive video, we’ll show you exactly how to build a production-ready AI Email Assistant that connects to Gmail, uses OpenAI GPT-4, and handles intelligent email automation using chat triggers, AI agent nodes, and OAuth authentication. Best of all, you won’t need to write a single line of code.

Ready to build your own AI-powered workflows? Access our FREE interactive labs where you can experiment with real n8n implementations, create your own AI agents, and see no-code automation in action!

🔧 Access KodeKloud KodeKey – https://kode.wiki/4ojmwCy

Tools Covered:
– n8n (open-source automation platform)
– OpenAI GPT-4
– Gmail API integration
– Google Cloud Console setup
– KodeKloud KodeKey for API keys

🚨Check out our complete n8n course on KodeKloud: https://kode.wiki/4qaLIwI

⏱️ Timestamps:
00:00 – Introduction to AI Agents & n8n
00:53 – Setting up n8n instance
01:29 – Creating your first workflow with trigger nodes
03:13 – Using KodeKey for API keys
04:55 – Alternative: Using OpenAI API keys directly
05:50 – Gmail And Google Cloud Console integration
07:06 – Creating OAuth credentials
09:27 – Testing your AI email agent
10:08 – Wrap up & key differences

🔔 Subscribe for more no-code AI tutorials!

#n8n #AIagent #NoCode #WorkflowAutomation #EmailAutomation #OpenAI #GPT4 #Gmail #AIworkflow #Automation #kodekloud #kodekloudlabs
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Gemini CLI: The Open Source Tool for AI Development
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Gemini CLI: The Open Source Tool for AI Development
🧪Try Gemini CLI Hands-On Labs for Free – https://kode.wiki/4oSoCdF …
🧪Try Gemini CLI Hands-On Labs for Free – https://kode.wiki/4oSoCdF

Discover Google’s brand new Gemini CLI and it’s about to replace half your manual work! This isn’t another code assistant – it’s …
a completely open-source terminal AI that actually runs commands, processes files, and automates entire workflows while you sleep.

The best part? It’s Apache 2.0 licensed, meaning you can customize this powerful automation engine for any commercial project without restrictions. While other AI tools lock you into their ecosystem, Google literally gave away the source code to their Gemini 2.5 Pro-powered terminal agent!

In this video, we’ll expose how companies are already using Gemini CLI to automate log translations, generate weekly reports from customer calls, and agentic file processing systems that require zero human intervention.

Ready to automate your entire workflow with AI? Access our FREE interactive labs where you can practice Gemini CLI syntax, build custom automation scripts, and deploy intelligent terminal-based agents!

🧪Try Gemini CLI Hands-On Labs for Free – https://kode.wiki/4oSoCdF

📚 What You’ll Learn:
• Why Gemini CLI beats Cursor, Claude-Code, and WindSurf for automation
• How to use Gemini CLI commands with file and directory references
• Open source advantages: Apache 2.0 license for commercial use
• Gemini CLI Demo

⏱️ Timestamps:
00:00 – Intro: Why Gemini CLI vs Cursor, Claude-Code, WindSurf
00:51 – When do I use Gemini CLI?
01:50 – Gemini CLI Use Cases
04:37 – Demo – Installing Gemini CLI
05:23 – Demo – Project Configuration
06:22 – Demo – Direct Prompting & Notation in Gemini CLI
07:30 – Demo – Server Log Translation using Gemini CLI
08:08 – Demo – Customer Service Analysis using Gemini CLI
08:42 – Summary & Free Lab Access

🔔 Subscribe for more AI tutorials!

#GeminiCLI #GoogleAI #googlegemini #GeminiPro #cursor #claudecode #windsurf #AIautomation #CommandLine #OpenSource #geminiclitutorial #AIagents #WorkflowAutomation #kodekloud #learnaiwithkodekloud
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Why LLMs Forget—and How RAG + Context Engineering Fix It (Free Labs).
Now Playing
Why LLMs Forget—and How RAG + Context Engineering Fix It (Free Labs).
🧪Hands-On Labs for Free – https://kode.wiki/4g4jXBx LLMs don’t truly …
🧪Hands-On Labs for Free – https://kode.wiki/4g4jXBx

LLMs don’t truly remember—most “memory” is just the context window, which varies by model and caps how much text fits in one turn, so this …
video shows how to engineer context and add RAG to simulate durable, grounded recall.

🧠 What You’ll Learn:
✅ Context window basics (2K to 1M tokens)
✅ Context engineering techniques
✅ RAG implementation with OpenAI API
✅ Demo using Free Labs

⏰ Timestamps:
00:00 – Why is Memory a big limitation in LLMs?
00:26 – Understanding context windows in LLMs
01:16 – How to Choose the right model for your task?
02:00 – Context engineering and the apple farm example
03:11 – How RAG works with vector databases
03:57 – One Drawback in RAG
04:30 – Lab introduction and setup
05:08 – Lab Demo – Setting Up the Environment
05:32 – Lab Demo – Context Window (The Pi Digit Problem)
06:14 – Lab Demo – Context Engineering
07:22 – Lab Demo – Memory Management
08:19 – Lab Demo – Simple RAG System
09:52 – Key takeaways and best practices

🧪 Hands-On Labs for Free – https://kode.wiki/4g4jXBx

🔔 SUBSCRIBE for cutting-edge AI tutorials that actually matter!

Check out our learning paths at KodeKloud to get started:
▶️ DevOps Learning Path: https://bit.ly/DevOpsLearningPath-YT
▶️ Cloud: https://kode.wiki/CloudLearningPath
▶️ Linux: https://bit.ly/LinuxLearningPath
▶️ Kubernetes: https://bit.ly/KubernetesLearningPath
▶️ Docker: https://bit.ly/DockerLearningPath
▶️ Infrastructure as Code(IAC): https://bit.ly/IACLearningPath
▶️ Programming: https://bit.ly/ProgrammingLearningPath

#LLM #ContextWindow #LargeLanguageModels #RAG #RetrievalAugmentedGeneration #VectorDatabase #LLMMemory #ContextEngineering #PromptEngineering #TokenLimits #GPT4 #ClaudeAI #GeminiPro #OpenAI #MachineLearning #NLP #AITutorial #AIMemory #LLMLimitations #AIEducation #DeepLearning #TransformerModels #AIContext #LongTermMemory #ShortTermMemory #VectorSearch #AIOptimization #LLMTraining #MemoryManagement #AILabs #HandsOnAI #PracticalAI #AIImplementation #LLMDevelopment #aitools #kodekloud

For more updates on courses and tips, follow us on:
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🌐 LinkedIn: https://www.linkedin.com/company/kodekloud/
🌐 Twitter: https://twitter.com/KodeKloudHQ
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🌐 Instagram: https://www.instagram.com/kodekloud/
🌐 Blog: https://kodekloud.com/blog/
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LangGraph Explained for Beginners
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LangGraph Explained for Beginners
🧪Try LangGraph Hands-On Labs for Free – https://kode.wiki/41WTH62 …
🧪Try LangGraph Hands-On Labs for Free – https://kode.wiki/41WTH62

Learn how LangGraph transforms simple LangChain chatbots into powerful AI agents with StateGraph, loops, and conditional workflows!

In this comprehensive video, we’ll show you …
exactly how to build a production-ready AI Research Assistant that searches the web, evaluates trustworthiness, extracts facts, and generates intelligent reports using nodes, edges, and shared state management.

Ready to build your own stateful AI workflows? Access our FREE interactive labs where you can experiment with real LangGraph implementations, create your own StateGraph architectures, and see agentic AI workflows in action!

🧪Try LangGraph Hands-On Labs for Free – https://kode.wiki/41WTH62

📚 What You’ll Learn:
• LangChain vs LangGraph: When to use each framework
• How StateGraph works with nodes, edges, and persistent memory
• State management patterns for production AI agents
• LangGraph Workflow Demo

⏱️ Timestamps:
00:00 – Introduction to LangGraph
00:20 – LangChain vs LangGraph: What’s the real difference?
01:19 – Deep Research Assistant Use Case Example
01:53 – Traditional approach pain points
02:21 – Orchestration in LangGraph
03:08 – What is StateGraph?
03:39 – LangGraph Workflow
04:42 – LangGraph Adoption in Business Requirements
05:16 – Demo – Installing LangGraph Ecosystem
05:50 – Demo – Sequential Workflow vs Stateful Workflow
06:29 – Demo – Chunking Strategy and Embedding
07:37 – Demo – StateGraph
08:29 – Demo – Nodes, Edges and Routing
09:38 – Demo – Loops and Iterations
10:15 – Demo – Tool Integration
10:45 – Demo – Memory and State
11:27 – Demo – Build Your Own Research Assistant
12:52 – Conclusion & Free Lab Access

🔔 Subscribe for more AI tutorials!


#LangGraph #LangChain #StateGraph #AIagents #AgenticAI #AIworkflows #AIautomation #LLM #OpenAI #Python #AutonomousAgents #workflowautomation #kodekloud
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