Build A LLM-Based Text Classifier| Prompt Engineering

🎬 In this video, I’ll show you how to build a text classification pipeline using open-source LLMs — with no fine-tuning, no API keys, and no OpenAI needed. We’ll use zero-shot and few-shot prompting to classify IMDb movie reviews with models like phi-2 from Hugging Face. You’ll learn how to: Prompt LLMs for classification tasks Compare zero-shot vs few-shot performance Evaluate accuracy and print mismatches Run everything in Colab or locally — free! Whether you're working on sentiment analysis, topic detection, or other NLP tasks, prompt engineering with LLMs is a powerful way to get started fast. 🔧 What You’ll Learn ✅ What is prompt engineering for LLMs 🔍 How zero-shot and few-shot prompting works 🧠 Use Hugging Face models like microsoft/phi-2 📈 Evaluate predictions and identify errors 📦 Resources 🧑‍💻 Code & Notebook: https://github.com/nachi-hebbar/LLM-Prompting