15. Stop Using One Giant Prompt! Build AI Workflows with LangGraph (Programming Assistant)
Stop Using One Giant Prompt! Build AI Workflows with LangGraph (Programming Assistant) #llm #agenticai #memory #langgraph
Github code link: https://github.com/NawazAli20/promptC...
Langgraph workflows:
https://docs.langchain.com/oss/python...
Video Timestamps:
0:0 Intro and recap of the previous lecture
0:25 Workflow diagram and theory of prompt chaining workflows
04:12 VS Code implementation of prompt chaining with Langgraph agent
6:55 Test the first node (code generation)
21:20 Create code explanation state (node)
25:00 Create complexity analysis state (node)
28: 00 Create final report state (node)
34:15 Complete the graph and compile
39:05 Invoke your final Langgraph to test the responses
42:35 Recap and looking for the future videos
Langsmith: https://smith.langchain.com/
Langsmith documentation: https://docs.langchain.com/langsmith/...
Langchain middleware:
https://docs.langchain.com/oss/python...
Langgraph's short-term memory: https://docs.langchain.com/oss/python...
Langchain tools integration: https://docs.langchain.com/oss/python...
Tavily search API key: https://app.tavily.com/playground
GROQ API key: https://console.groq.com/playground
Openweather map (3rd party API): https://openweathermap.org/
Langchain pydanctic documentation:
https://docs.langchain.com/oss/python...
Github Chatbot code link: https://github.com/NawazAli20/LLMChatBot
Github LLMs Onboarding code link:
https://github.com/NawazAli20/LLMsIntro
Complete playlist: • 🚀 LLMs & Agentic AI Masterclass (2026) | ...
Master Large Language Models (LLMs) and Agentic AI by building real-world AI applications from scratch using Python. This hands-on playlist takes you from the fundamentals to production-ready AI systems with step-by-step coding, practical projects, and best practices.
You'll learn how to build intelligent AI agents using LangChain, LangGraph, LiteLLM, LangSmith, Ollama, OpenAI, and other leading frameworks.
📚 What You'll Learn
✅ UV & Python Environment Setup
✅ LLM APIs & Prompt Engineering
✅ Structured Outputs & Tool Calling
✅ AI Agents & Deep Agents
✅ Memory & Conversation Management
✅ LangChain & LangGraph
✅ RAG & Agentic RAG
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✅ Fine-Tuning LLMs
✅ MCP for external tools and service
✅ Guardrails & AI Safety
✅ LLM Evaluation & LangSmith Observability
✅ Local LLMs with Ollama
✅ Production Deployment (Local & Cloud)
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