LLM

Understanding the Rerank Stage in Industrial RAG Pipelines

Retrieval-Augmented Generation (RAG) systems and modern search engines rely on multiple stages to retrieve the most relevant information for a user query. One critical component in these pipelines is Rerank, a stage designed to improve the precision of retrieved results.

Query Rewrite in RAG Systems: Why It Matters and How It Works

In Retrieval-Augmented Generation (RAG) systems, many developers focus heavily on embeddings and vector databases. However, in real-world production systems, one of the most critical components is often overlooked:

Retrieval Strategy Design: Vector, Keyword, and Hybrid Search

This article explains how to design a modern retrieval strategy for AI systems, especially Retrieval-Augmented Generation (RAG). The focus is not only on definitions, but on engineering trade-offs, system architecture, and practical defaults.

Designing a Scalable Knowledge Base for Large Language Models

A Practical Engineering Guide to Cleaning, Semantic Chunking, Metadata, and Batch Embeddings

How to Choose the Right Model for Your AI Application

Choosing an AI model is not about finding the strongest model.