Artificial intelligence
LLMs, RAG, embeddings, agents, and the tools engineers actually use, explained without the hype.
Latest in Artificial intelligence
- LangChain for RAG: A Beginner's Guide
Learn LangChain for RAG with Python: load and split documents, embed them, build a retriever, stream answers with sources, and try agentic RAG.
- How to Use the OpenAI API in a RAG Pipeline (Python)
Learn how to use the OpenAI API in a RAG pipeline with Python: embeddings, the Responses API, streaming, structured outputs, chat history, and costs.
- Build a Simple RAG Application Using Python (Step-by-Step)
Build a simple RAG application in Python from scratch with OpenAI embeddings and NumPy. Step-by-step code for chunking, retrieval, and grounded answers.
- How Document Chunking Works in RAG
When a RAG system receives a large document, it usually doesn't send the entire document to the AI model at once. Instead, the document is broken into smaller pieces called chunks.
- Semantic Search vs Keyword Search : What's the Difference ?
Keyword search finds matching words. Semantic search tries to find matching meaning. That's the simplest way to understand the difference. If you search for :
- Vector Databases Explained: How They Work in RAG
A vector database is a database designed to store, index, and search numerical vectors efficiently. In a RAG application, vector databases are commonly used to store embeddings of documents and retrieve the pieces of inf
- What Are Embeddings? A Developer-Friendly Guide to Embeddings in AI
Embeddings are numerical representations of data that capture meaning, relationships, and patterns in a form that machine learning systems can compare.
- What Is RAG ? Retrieval-Augmented Generation Explained
Learn what RAG is, how Retrieval-Augmented Generation works, its architecture, benefits, limitations, and why developers use RAG for AI applications.
- GPT-6 Astra and the New Era of AI Agents
GPT-6 Astra is changing AI agents with advanced reasoning, computer use, coding, research, and automation. Here's what developers need to know.
- DSA for AI Engineers: 10 Data Structures You Should Know
Artificial intelligence isn't built only with neural networks and machine learning models. Behind many AI systems are familiar data structures and algorithms that store information, search data, rank results, process req
- Google Antigravity 2026: Features & How to Use
Google Antigravity is Google's agentic development platform. Learn what Antigravity 2.0 is, how it works, its key features, CLI, Gemini 3.8 Flash support, and how developers can use it.
- MCP Resources Explained with Real Examples
Learn what MCP Resources are, how they work, and how to implement them with real-world examples. A beginner-friendly guide to Model Context Protocol Resources.
- MCP Tools Explained: Build and Expose Your Own Tools
MCP tools allow an AI model like ChatGPT or Claude to perform real actions instead of just generating text.
- Build Your First MCP Client from Scratch (2026): A Beginner-Friendly Tutorial
Learn how to build your first MCP Client from scratch using Node.js. This beginner-friendly tutorial covers setup, architecture, code examples, and best practices.
- Build Your First MCP Server: A Hands-On Tutorial
Build your first MCP server with Python in this beginner-friendly tutorial. Learn how to create MCP tools, run your server, test it with MCP Inspector, and understand how MCP works through practical examples.
- AI Coding Agents in 2026 : How Agentic AI Is Changing Software Development
AI coding agents can now inspect repositories, modify files, run tests, fix bugs, and create pull requests. Here’s what developers need to know about agentic coding in 2026.
- Model Context Protocol (MCP) Explained: The Complete Developer Guide
Learn what the Model Context Protocol (MCP) is, how MCP clients and servers work, and why it is becoming the standard for connecting AI models with external tools, APIs, databases, and applications.