Data Engineering Insights

RAG in Production: The Complete 11-Step Pipeline

From chunking strategies to evaluation metrics, everything a data engineer needs to build RAG systems that actually work

The Modern AI Stack Is Bigger Than Most People Think

Everyone wants to build AI applications. But most tutorials stop at the API call. Calling an LLM is not the hard part. The hard part is everything around it: the data pipelines that feed it, the retrieval systems that give it context, the evaluation that stops it from hallucinating in production, and the monitoring that tells you when it breaks. This is the full stack.

Claude Models Explained: Which One Should You Use?

A beginner-friendly guide to the Claude model family. Learn what Haiku, Sonnet, Opus, and Fable 5 each do best, how they differ in speed and cost, and how to pick the right one for your project.

RAG Explained: Teaching AI to Search Before It Speaks

A beginner-friendly guide to Retrieval-Augmented Generation — how AI can search your documents before answering. Covers the full RAG pipeline, embeddings, vector databases, and how it compares to MCP.

MCP Explained: The Bridge Between AI and the Real World

A beginner-friendly guide to MCP, how AI models connect to databases, files, APIs, and real-world tools

LLM, RAG, MCP, or Agent — How to Pick the Right Architecture for AI in Data Engineering

Before you choose any AI technology — understand your project first. What does it need? What problem should AI solve? This article gives you a clear framework to decide which AI layer fits your data engineering project.

From Text to Thought: How RAG and MCP Are Changing the Way AI Works

Data Engineering Roadmap: How Every Data Engineer Should Start

A complete step-by-step roadmap for anyone who wants to become a data engineer — covering Python, SQL, cloud platforms, streaming, and Infrastructure as Code. Based on 20+ years of real industry experience.