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 u0026 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

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