Creating
Creating a Knowledge Graph Using an LLM
In this tutorial, we’ll show how to create a Knowledge Graph from an unstructured document using an LLM. While traditional NLP methods have been used for extracting entities and relationships, Large Language Models (LLMs) like GPT-4o-mini make this process more accurate and context-aware. LLMs are especially useful when working with messy, unstructured data. Using Python,…
Creating The “Moving Highlight” Navigation Bar With JavaScript And CSS — Smashing Magazine
In this tutorial, Blake Lundquist walks us through two methods of creating the “moving-highlight” navigation pattern using only plain JavaScript and CSS. The first technique uses the getBoundingClientRect method to explicitly animate the border between navigation bar items when they are clicked. The second approach achieves the same functionality using the new View Transition API….
Step-by-Step Guide to Creating Synthetic Data Using the Synthetic Data Vault (SDV)
Real-world data is often costly, messy, and limited by privacy rules. Synthetic data offers a solution—and it’s already widely used: LLMs train on AI-generated text Fraud systems simulate edge cases Vision models pretrain on fake images SDV (Synthetic Data Vault) is an open-source Python library that generates realistic tabular data using machine learning. It learns…
Creating an AI-Powered Tutor Using Vector Database and Groq for Retrieval-Augmented Generation (RAG): Step by Step Guide
Currently, three trending topics in the implementation of AI are LLMs, RAG, and Databases. These enable us to create systems that are suitable and specific to our use. This AI-powered system, combining a vector database and AI-generated responses, has applications across various industries. In customer support, AI chatbots retrieve knowledge base answers dynamically. The legal…
Creating An AI Agent-Based System with LangGraph: A Beginner’s Guide
What is an Agent? An agent is a Large Language Model (LLM)-powered system that can decide its own workflow. Unlike traditional chatbots, which operate on a fixed path (ask → answer), agents are capable of: Choosing between different actions based on context. Using external tools such as web search, databases, or APIs. Looping between steps…