Build and Deploy an AI Chatbot Using LLMs, Python, RunPod, Hugging Face, and React Native

RunPod: https://rebrand.ly/Runpod-Abdullah 🚀 Introduction: ================================ In this tutorial, we’ll build and deploy a complete coffee shop customer service AI chatbot that takes orders, provides menu info, blocks irrelevant conversations, and even recommends products based on Market Basket Analysis! We'll cover cutting-edge topics like Prompt Engineering, Retrieval-Augmented Generation (RAG), and the modular power of Agent-Based Systems. You'll also learn how to deploy Large Language Models (LLMs) and custom APIs using RunPod, and build a full React Native app that connects to Firebase and the RunPod endpoints. By the end, you’ll have a fully functional chatbot app and level up your AI, development, and deployment skills! 🔄 Update: Runpod seems to have changed the place to access the openAI URL here it is: https://api.runpod.ai/v2/{RUNPOD_ENDPOINT_ID}/openai/v1 and the RunPod endpoint ID is the string right under the endpoint name. here is a link for more information: https://docs.runpod.io/serverless/workers/vllm/openai-compatibility 💡 What You’ll Learn: ================================ 1. 🧠 Prompt Engineering: Guide your chatbot with precise instructions. 2. 🔍 Retrieval-Augmented Generation (RAG): Enhance chatbot answers using personalized data. 3. 🛠️ Agent-Based Systems: Create specialized components for efficient and accurate chatbot responses. 4. 📊 Market Basket Analysis Recommendation Engine: Build a recommendation engine from scratch. 5. 🖥️ RunPod Deployment: Deploy LLMs, embedding models, and custom APIs effortlessly. 6. 📱 React Native App: Build an end-to-end mobile app connected to Firebase and RunPod. 🔗 Links: ================================ RunPod: https://rebrand.ly/Runpod-Abdullah Github Repo: https://github.com/abdullahtarek/coffee_shop_customer_service_chatbot Coffee Shop Transactions Kaggle Dataset Link: https://www.kaggle.com/datasets/ylchang/coffee-shop-sample-data-1113 Figma design link: https://www.figma.com/desi