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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