VectorDB

A dashboard showing a RAG system answering questions

Build an End-to-End RAG System for Your Portfolio

Learn to build a production-style RAG pipeline that demonstrates end-to-end systems engineering, from data ingestion to API serving.

Diagram of a retrieval‑augmented generation architecture with data pipelines and vector store.

Architecting Production Retrieval-Augmented Generation: Scalable Data Pipelines, Vector Stores, and Reliability Patterns

A deep dive into designing RAG services at scale, covering data ingestion pipelines, vector database choices, and fault‑tolerant patterns used by modern AI teams.

Optimizing RAG Performance with Advanced Metadata Filtering and Vector Database Indexing Strategies

Introduction Retrieval‑Augmented Generation (RAG) has quickly become the de‑facto architecture for building LLM‑powered applications that need up‑to‑date, factual, or domain‑specific knowledge. By coupling a large language model (LLM) with a vector store that holds embedded representations of documents, RAG lets the model “look up” relevant passages before it generates an answer. While the conceptual pipeline is simple—embed → store → retrieve → generate—real‑world deployments quickly expose performance bottlenecks. Two of the most potent levers for scaling RAG are metadata‑based filtering and vector database indexing strategies. Properly harnessed, they can: ...