RAG (Retrieval-Augmented Generation) from Scratch
Large language models are powerful, but they do not know your data. Retrieval-augmented generation (RAG) solves that gap by combining...
LangChain Fundamentals: Chains, Agents, and Memory
LangChain is often introduced as a convenience library for LLM apps. In practice, it is an architectural toolkit. Teams that...
Getting Started with Claude API: Messages, Tools, and Streaming
If you are integrating large language models into real applications, the API design matters as much as the model itself....
Building Apps with OpenAI API: Chat, Embeddings, and Function Calling
Large language models are no longer experimental tools. They are now core infrastructure for modern applications. However, many teams struggle...
Google Antigravity Editor: What It Is and How It Changes Your Workflow
If you have been using Cursor, VS Code + Copilot, or similar tools, you already understand the baseline promise: AI...
Creating a Chatbot for Developer Documentation with Open‑Source LLMs
Introduction Developer documentation is critical, but searching through hundreds of pages of API references, tutorials, and wikis can be frustrating....
The Future of AI in Software Development (2026 trends)
Artificial intelligence has already transformed software development with tools like GitHub Copilot, ChatGPT, and AI-powered code reviews. However, we’re still...
AI‑Powered Pair Programming: best practices for collaborating with an AI assistant
Introduction Pair programming has always been about two developers working together to write better code—one typing while the other reviews...
Generating Unit Tests with Large Language Models: Benefits and Risks
Unit tests are essential for ensuring code quality and catching bugs early. However, writing them manually consumes significant development time,...