DEV Community

# rag

Retrieval augmented generation, or RAG, is an architectural approach that can improve the efficacy of large language model (LLM) applications by leveraging custom data.

Posts

👋 Sign in for the ability to sort posts by relevant, latest, or top.
Context Engineering: The Missing Piece in Building AI Agents That Actually Work

Context Engineering: The Missing Piece in Building AI Agents That Actually Work

Comments 1
4 min read
OrKa 0.9.4: cleaner logs, full GraphScout paths, ISO timestamps

OrKa 0.9.4: cleaner logs, full GraphScout paths, ISO timestamps

1
Comments
1 min read
I Created an AI Assistant That Reads the Fine Print for You

I Created an AI Assistant That Reads the Fine Print for You

6
Comments 1
4 min read
RAG for Dummies

RAG for Dummies

5
Comments
2 min read
**Processing Mode**

**Processing Mode**

Comments
3 min read
Vector Databases Guide: RAG Applications 2025

Vector Databases Guide: RAG Applications 2025

4
Comments
10 min read
LLPY-03: Extracción y Procesamiento Inteligente de Datos Legales

LLPY-03: Extracción y Procesamiento Inteligente de Datos Legales

Comments
21 min read
💡 What's new in txtai 9.0

💡 What's new in txtai 9.0

1
Comments
5 min read
RAG: experiments with prompting using 3 LLM's

RAG: experiments with prompting using 3 LLM's

Comments 2
8 min read
Exposing the Magic of Large Language Models Like ChatGPT Explained Simply for CEOs and Lawyers

Exposing the Magic of Large Language Models Like ChatGPT Explained Simply for CEOs and Lawyers

Comments
4 min read
How I Built an AI Workspace To Help Students & Researchers

How I Built an AI Workspace To Help Students & Researchers

Comments
2 min read
Day 7 — FAISS empty vectors, metric mismatch, and recall collapse (ProblemMap No.8)

Day 7 — FAISS empty vectors, metric mismatch, and recall collapse (ProblemMap No.8)

Comments
3 min read
Build Agentic Video RAG with Strands Agents and Containerized Infrastructure

Build Agentic Video RAG with Strands Agents and Containerized Infrastructure

15
Comments
6 min read
How We Used RAG to Power an AI-First Internal Tool Builder

How We Used RAG to Power an AI-First Internal Tool Builder

Comments
2 min read
LLPY-02: Configurando un Entorno de Desarrollo Moderno con UV

LLPY-02: Configurando un Entorno de Desarrollo Moderno con UV

Comments
5 min read
AI Made Simple: Understanding LLMs, RAG, and MCP Servers 🤖

AI Made Simple: Understanding LLMs, RAG, and MCP Servers 🤖

Comments
2 min read
🚀 Sample RAG app with Strands, Reflex and S3

🚀 Sample RAG app with Strands, Reflex and S3

8
Comments
2 min read
From Documents to Dialogue: A step-by-step RAG Journey

From Documents to Dialogue: A step-by-step RAG Journey

1
Comments 1
5 min read
But what is “contextual search” — case study of KENDO-RAG and how it beats Google for private data

But what is “contextual search” — case study of KENDO-RAG and how it beats Google for private data

8
Comments
7 min read
is RAG dead? nope—it learned to drive

is RAG dead? nope—it learned to drive

Comments
1 min read
From Zero to 1 B Vectors: the 2025 No-BS Picking Guide

From Zero to 1 B Vectors: the 2025 No-BS Picking Guide

1
Comments
2 min read
🤖 AI Web Scraper & Q&A

🤖 AI Web Scraper & Q&A

Comments
4 min read
Semantic Embedding in RAG: why close vectors still miss meaning and how to fix it

Semantic Embedding in RAG: why close vectors still miss meaning and how to fix it

Comments
4 min read
LLPY-01: Construyendo un Sistema RAG para Derecho Laboral Paraguayo

LLPY-01: Construyendo un Sistema RAG para Derecho Laboral Paraguayo

Comments
4 min read
Agentic vs Graph RAG: Two paths to smarter AI systems

Agentic vs Graph RAG: Two paths to smarter AI systems

1
Comments 1
3 min read
loading...