Don't learn AI Agents without Learning these Fundamentals

🧪AI Agents Labs for Free:

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:

⏰ 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