From Scratch AI

Build a Smart Netflix Dashboard That Answers You with Charts What if you could ask a dataset a question—and get back a chart? In this beginner-friendly tutorial, you’ll create a Streamlit app that does exactly that: Loads Netflix data into your browser Lets you ask questions using plain English Uses AI to answer your question and dynamically generate a chart No coding experience? No problem. Follow these steps and you'll have it running locally in minutes. 🧰 What You’ll Need Python installed from python.org Netflix dataset from Kaggle - Netflix Shows A free OpenAI API key from platform.openai.com You may need to pre-purchase credits to use the API in this sample app. 📁 Setup Instructions Create a folder called netflix-ai-dashboard Place netflix_titles.csv inside that folder Install the required Python libraries: pip install streamlit pandas matplotlib openai 📄 Full App Code ( app.py ) Create a new f...

Building an AI app sounds complicated—but it doesn’t have to be. In this tutorial, you’ll create a simple Retrieval-Augmented Generation (RAG) app that extracts text from PDFs and answers questions about them using an AI model like Claude. Even if you’re brand new to AI, this guide will walk you through every step. By the end, you’ll have a basic app that: Accepts a PDF file Extracts its content Lets you ask questions about it Returns AI-powered answers What You'll Need / Dependencies: To follow along, install the following: Python (3.8+) Download: https://www.python.org/downloads/ Virtual Environment (recommended) Create one in your project folder: python -m venv venv source venv/bin/activate # On Windows: venv\Scripts\activate Install required packages: In your terminal, run: pip install streamlit pypdf sentence-transformers faiss-cpu anthropic ...

Ever wished your AI assistant could answer questions using your data or documents, not just what it was trained on? That’s exactly what Retrieval-Augmented Generation (RAG) does—and it’s easier to understand than it sounds! In this post, you’ll learn what RAG is, how it works (in plain English), and why it’s so useful—even if you’re just getting started with AI. Let’s go! What You'll Need / Dependencies: No advanced AI knowledge required! Just a basic understanding of how AI chatbots (like ChatGPT or Claude) give answers based on what they were trained on. Step-by-Step: How RAG Works Start with a user question: For example: “What’s our company’s policy on vacation time?” Search your documents: RAG uses a smart search system (like a vector database) to find the most relevant chunks from your own files, PDFs, web pages, or knowledge base. Feed those results into an AI model: The AI reads those chunks and uses them to generate a helpful, grounded answer. ...

Welcome to your first AI-powered assistant tutorial! In this guide, you'll build a simple, interactive AI assistant using Streamlit and the Claude API. By the end, you'll have a fully functioning web app capable of answering questions using artificial intelligence—no fancy coding skills required. Let's dive in! What You'll Need / Dependencies: Here's what you'll need to follow along: Python (version 3.8 or higher): Download Python . Claude API Key: Sign up for a free API key from Anthropic . Streamlit: An easy-to-use framework to create web apps quickly. Anthropic Python library: To communicate with the Claude API. Installing Dependencies: Create a new folder, open your terminal (or command prompt), and navigate into it. Run these commands: python -m venv venv source venv/bin/activate # (macOS/Linux) .\venv\Scripts\activate # (Windows) pip install streamlit anthropic Step-by-Step Instructions: Step 1: Setting Up Your Project S...

Ever dreamed of creating your own interactive web apps but thought coding was too complicated? Streamlit changes all of that! In this beginner-friendly guide, you'll build your very first web application from scratch—no complicated setup required. By the end, you'll have a working, shareable app and the confidence to create even more exciting projects. Let’s jump in! What You'll Need / Dependencies: Python : Download and install Python from python.org (version 3.8 or higher recommended). pip (Python package installer) : Comes bundled with Python—no separate installation needed. Streamlit : Install with the following command in your terminal or command prompt: pip install streamlit That's it! You're ready to create your first Streamlit app. Step-by-Step Instructions: Step 1: Create Your Project Folder Create a new folder on your desktop called streamlit_app . Step 2: Create Your Python Script Inside the streamlit_app folder, create a file named app....

Ready to Call Your First AI API? Ever heard about Claude—the friendly, helpful AI model created by Anthropic? Today, you'll learn how to write your very first Python script to interact with Claude's API. By the end, you'll have a simple script that sends your question to Claude and receives a clear, human-like response. No fancy skills required—let's dive right in! What You'll Need / Dependencies: You'll need a few things set up on your computer before we start: Python 3.x: Download from python.org. pip (Python package manager): Usually comes with Python by default. Anthropic Python library: Install easily using pip. Claude API Key: Sign up at Anthropic.com to get your key. Here's how to install the required library using pip: pip install anthropic After installing, save your Claude API key in a safe place—we'll use it shortly! Step-by-Step Instructions: Step 1: Import the Anthropic library Open your favorite code editor and start a new Python ...

If you're excited about diving into the world of Artificial Intelligence but don't know where to start—don't worry! This guide will walk you step-by-step through setting up Python, the most popular programming language for AI development. By the end, you'll have everything ready to start coding your first AI application. No prior programming experience needed! What You'll Need / Dependencies: Python: The main programming language for AI projects. We'll install the latest version. pip: Python’s package manager that makes installing libraries super easy. Anaconda (Optional but Recommended): Provides Python and popular data science tools pre-installed. AI Libraries: NumPy, Pandas, Matplotlib, and Scikit-learn. Code Editor (Optional but Recommended): Visual Studio Code. Step-by-Step Instructions: 1. Installing Python Windows & Mac: Download the latest Python installer from python.org . Run the installer, making sure you check the box that sa...

Have you ever wondered why sometimes AI gives you exactly what you need—and other times it feels way off? The secret lies in how you ask it. In this post, you’ll learn how to create crystal-clear prompts so that any AI model (think ChatGPT, Claude, or similar) understands precisely what you want—and delivers it. No prior experience needed! What You’ll Need / Dependencies An AI tool or account: Access to ChatGPT (free or Plus), the OpenAI API, or another conversational AI platform. Optional: Python & OpenAI SDK: If you want to experiment programmatically, install the OpenAI package: pip install openai And set your OPENAI_API_KEY in your environment. Text editor or notebook: Any tool where you can draft and refine your prompts (e.g., VS Code, Jupyter, even a plain text file!). Curiosity! You don’t need special expertise—just a willingness to experiment and iterate. Step-by-Step Instructions Define your objective Before writing anything, ask...

Let’s be real: AI is everywhere these days, but what’s actually powering tools like ChatGPT, Claude, or Gemini? The answer is Large Language Models, or “LLMs.” If you’ve ever wondered what that means (or were just too polite to ask), this guide is for you! By the end, you’ll know what an LLM is, how it works in plain English, and why these models are such a big deal in the world of artificial intelligence. What You'll Need / Dependencies No technical background required! This guide is written for everyone. To try out LLMs yourself: A computer with internet access (for online demos) Optional: A free OpenAI account at chat.openai.com , or similar platforms like Claude.ai or Google Gemini To experiment locally (optional, for curious coders): Python 3.8 or newer Pip (Python’s package installer) Install the transformers library: pip install transformers You do NOT need to code to follow this guide! Step-by-Step Instructions 1. What is a Large Language Model (LLM)...

Welcome to the world of AI! If you’ve ever wondered how people get useful, creative, or even jaw-dropping results from AI tools like ChatGPT, Claude, or Gemini, it all starts with writing a good prompt. In this beginner-friendly guide, I’ll walk you through everything you need to know to create your very first AI prompt—even if you have zero experience. By the end, you’ll know exactly how to start, what to say, and how to get the most out of any AI chatbot. What You'll Need / Dependencies Before we dive in, let’s make sure you have everything set up: An AI tool or chatbot: For beginners, ChatGPT (free version available), Claude , or Gemini are all great options. Sign up if you don’t have an account yet. Internet connection: All major AI chatbots are web-based—just open your browser! No coding or technical setup required. Seriously, all you need is a browser and curiosity. That’s it! No special downloads, no secret software. Step-by-Step Instructions 1. Open Your AI ...

Welcome! Let's Explore AI and Prompt Engineering If you’ve heard all the buzz about “AI” and “prompts” but have no idea where to start—don’t worry, you’re in the right place. In this post, we’ll demystify what AI really is, show you how prompt engineering works (in simple language!), and walk you through your very first steps to create and use your own AI prompts. No technical background required. Let’s dive in! What You’ll Need / Dependencies Internet Connection — You’ll use web-based AI tools. A Free Account on an AI Platform — Sign up for ChatGPT or Claude (both are beginner-friendly). Curiosity! — That’s it. No special software or coding needed for this first adventure. Step-by-Step Instructions 1. What Is AI, Really? AI stands for Artificial Intelligence . In simple terms, it’s a computer system that can do tasks that normally need human smarts—like understanding language, recognizing images, or making decisions. Today, the most common kind you’ll interact wit...

Ready to take prompt engineering further? Let’s talk about dynamic prompt injection —a powerful technique where you inject real-time data into your prompts, enabling your AI assistant to deliver hyper-relevant, personalized results every time. 🌟 What is Dynamic Prompt Injection? Dynamic prompts combine your instruction with variables or external data. Rather than static prompts, dynamic prompts update based on inputs, context, or external data sources. 🔗 Why Use Dynamic Prompts? Personalization: Tailor AI responses specifically to individual users. Real-time insights: Provide up-to-date information directly in the prompt. Automation: Reduce repetitive manual inputs, improving efficiency. 🛠️ How to Create a Dynamic Prompt Dynamic prompts typically follow this formula: "Instruction {variable/data} additional context." Example: "You're a financial advisor. Given today's stock market data: {current_stock_data}, suggest three stocks...

You already know the basics—now it's time to level up. In this tutorial, we'll explore advanced prompt engineering techniques to help you get even more from Claude, ChatGPT, and other LLMs. Ready to get serious? ⚙️ Understanding Advanced Prompt Engineering Advanced prompt engineering involves: Chained Prompts: Breaking tasks into smaller, manageable prompts Few-Shot Learning: Guiding AI with structured examples Prompt Templates: Creating reusable prompts for efficiency System Prompts: Setting context and tone clearly upfront Prompt Optimization: Iterating and refining prompts for accuracy 🔗 Technique 1: Chained Prompts (Prompt Chaining) Sometimes tasks are too complex for one prompt. Solution? Break it down. # Prompt 1 (Outline): "Create an outline for a blog post about time management techniques." # Prompt 2 (Expand each part): "Write a paragraph about the Pomodoro Technique based on this outline." # Prompt 3 (Polish): ...

You've built your own Claude-powered RAG (Retrieval-Augmented Generation) app. It works beautifully on your computer… but now you're ready for the next step: 🌍 Make It Public In this guide, you’ll learn how to host your AI app on the web using Streamlit Community Cloud — a free and beginner-friendly platform that gets your app online in minutes. No servers, no stress. --- 🧰 What You’ll Need Item Purpose GitHub account Store and share your app’s code Streamlit Cloud account Free hosting for Streamlit apps .env file Keep your API keys secret requirements.txt Tell Streamlit what libraries to install --- 📦 Step 1: Upload Your Code to GitHub Create a free GitHub account if you don't have one: github.com Create a new repo (e.g., claude-rag-app ) Add your app files: app.py rag_helpers.py (if applicable)...

So you've played with basic RAG (Retrieval-Augmented Generation), but now you're ready to level up. In this tutorial, you’ll learn how to build a smarter RAG system that can: 💾 Ingest multiple documents (PDF or TXT) 🔍 Use embeddings for fast, relevant search 🤖 Generate responses using Claude based on the most relevant content This is perfect for anyone creating a smart knowledge assistant, internal wiki search, or custom Q&A bot. 🎯 What We’ll Use Tool Purpose Python Core logic Streamlit User interface Claude API Answer generation OpenAI / TensorFlow Embeddings FAISS Efficient vector search PyMuPDF PDF text extraction --- 📦 Step 1: Install the Tools pip install streamlit faiss-cpu openai python-dotenv PyMuPDF anthropic tiktoken 🔐 Create a .env file with: OPENAI_...

Ever wished ChatGPT or Claude could answer questions using your own documents or website content? That’s exactly what Retrieval-Augmented Generation (RAG) does. It’s like giving your AI assistant access to a custom knowledge base — so it can generate smarter, more relevant answers. 🔍 What is RAG in Simple Terms? RAG combines two powerful steps: Retrieval: It searches a set of documents (like PDFs, websites, or notes) to find relevant chunks. Generation: It feeds those chunks into an LLM (like Claude or GPT) to generate an accurate, helpful answer. Think of it like this: User → “What are the steps for applying for a mortgage?” RAG → Searches your finance PDFs → Finds a page with the steps → Sends it to Claude → Claude writes a helpful answer based on that info. 📦 What You'll Build In this tutorial, we’ll create a simple RAG system that: 📝 Lets you upload a PDF or TXT file 🔍 Searches that file for relevant content 🤖 Sends it to Claude to answer use...

Whether you're chatting with ChatGPT, Claude, or any other large language model (LLM), your results depend on one crucial skill: prompt engineering . Think of it as talking to a genie — the better your wish, the better the magic. ✨ What Is Prompt Engineering? Prompt engineering is the art and science of crafting instructions for AI tools to get useful, accurate, and creative results. You're not just asking questions — you're designing conversations that guide the model's thinking. 🧩 Prompt = Instruction + Context + Output Format Every solid prompt has three pieces: Instruction: What do you want it to do? Context: What info does it need to do it well? Output Format: How should it respond? Example: "You're a witty travel agent. Suggest a weekend trip for a couple who loves food and history. Give me 3 destinations with pros and cons in bullet format." 🎮 Fun Exercise #1: Fix the Prompt Here’s a weak prompt: "Write about ...

Once you've crafted a great prompt, don't let it go to waste! In this guide, you'll learn how to turn any prompt into a reusable, personalized template — perfect for creating your own AI-powered toolkit. 🔁 Why Use Prompt Templates? 📌 Save time on tasks you do repeatedly 📈 Maintain consistency and quality in outputs 🔧 Easily customize for different situations 📄 Step 1: Identify Reusable Prompts Think about prompts you’ve used that worked well. Do they fit into common categories? Writing emails Creating content ideas Summarizing meetings Outlining blog posts 🔤 Step 2: Add Placeholders Turn static text into dynamic templates by replacing specifics with brackets. For example: Original Prompt: Write a friendly follow-up email to a client named Sarah about their recent website inquiry. Keep it short and helpful. Template Version: Write a friendly follow-up email to a client named [Client Name] about their recent [Topic]...

So far, you’ve learned the basics of prompt engineering and built a custom assistant. Now, let’s take it up a notch by creating a multi-step AI workflow — using prompt chaining. We’ll walk through a real-world task: drafting the perfect email. 🔗 What is Prompt Chaining? Prompt chaining means using the output of one AI prompt as the input for the next. It’s a way to break complex tasks into smaller, manageable steps — and guide the AI to better results. ✉️ Use Case: Writing a Polished Email We’ll guide the AI through 3 steps: Generate key points Create a rough email draft Polish the final version 🧱 Step 1: Gather Key Points You are a professional assistant. Ask the user what the email is about, who it's for, and what tone they want to use. Then summarize the key points to include. Example Input: I need to email a client about rescheduling our meeting. I want to stay polite but firm. They’ve rescheduled twice already. Example Output: Key Points: ...

Now that you understand the basics of prompt engineering, it’s time to build something useful — your very own AI assistant! In this tutorial, we’ll walk through how to design a practical, reusable assistant with structured prompts — no coding needed. 🚀 What You’ll Learn How to design a system prompt for a specific role How to structure inputs for repeatable use How to adjust tone and output style 👩‍💼 Example Assistant: Productivity Coach This AI assistant will help you plan your day, prioritize tasks, and offer productivity tips. You can copy and paste this prompt into ChatGPT, Claude, or your favorite AI tool. 🧰 Prompt Template: You are a friendly and focused productivity coach. Your job is to help the user plan their day, prioritize 3–5 tasks, and give one tip to stay focused. Ask the user what their goals are today, what’s urgent, and how much time they realistically have. Be concise, supportive, and clear. Example Input: Today I need to: - Finish ...

Prompt engineering is the art of crafting effective input for AI language models like ChatGPT, Claude, and others. In this guide, we'll walk you through what prompt engineering is, why it matters, and how to get started — all with simple, friendly examples. 🔍 What is Prompt Engineering? Prompt engineering is the process of writing instructions (prompts) that guide an AI model to produce the kind of response you want. Think of it like talking to a very smart assistant — the better you explain what you need, the better the help you'll get. 💡 Why It Matters Better Results: Well-crafted prompts lead to more accurate and useful answers. Efficiency: You reduce trial and error by being clear and structured. Creativity: You can unlock storytelling, summaries, code generation, and more. 🛠️ Getting Started with Prompt Engineering Here’s a simple framework you can use: Prompt Formula: [Role] + [Task] + [Context] + [Constraints] + [Examples...