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.
Beyond Vanilla RAG: The 7 Modern RAG Architectures Every AI Engineer Must Know

Beyond Vanilla RAG: The 7 Modern RAG Architectures Every AI Engineer Must Know

1
Comments
15 min read
Vector Stores for RAG Comparison

Vector Stores for RAG Comparison

Comments
7 min read
Retrieval-Augmented Generation: Connecting LLMs to Your Data

Retrieval-Augmented Generation: Connecting LLMs to Your Data

Comments
10 min read
Retrieval-Augmented Generation (RAG) Agents: How to Build Grounded, Tool‑Using GenAI Systems

Retrieval-Augmented Generation (RAG) Agents: How to Build Grounded, Tool‑Using GenAI Systems

Comments
9 min read
Beyond Keyword Search: How LangChain's Self-Query Retriever Transforms Natural Language Into Smart Filters -Part-I

Beyond Keyword Search: How LangChain's Self-Query Retriever Transforms Natural Language Into Smart Filters -Part-I

1
Comments
6 min read
How Kiro’s Global Steering Turned Me Into a Solo Frankenstein Engineer

How Kiro’s Global Steering Turned Me Into a Solo Frankenstein Engineer

Comments
2 min read
GraphRAG : From Zero to Hero

GraphRAG : From Zero to Hero

Comments
4 min read
The Boring Debug Checklist That Fixes Most “RAG Failures”

The Boring Debug Checklist That Fixes Most “RAG Failures”

Comments
2 min read
Building a Local RAG for Agentic Coding: From Fixed Chunks to Semantic Search with Keyword Boost

Building a Local RAG for Agentic Coding: From Fixed Chunks to Semantic Search with Keyword Boost

1
Comments
9 min read
RAG vs Document Injection: Why Your AI Document Chat Needs Smart Retrieval

RAG vs Document Injection: Why Your AI Document Chat Needs Smart Retrieval

Comments
6 min read
How LLM use MCPs?

How LLM use MCPs?

6
Comments
2 min read
Neo4j GraphRAG: Intelligent Knowledge Graph Querying with AI

Neo4j GraphRAG: Intelligent Knowledge Graph Querying with AI

Comments
11 min read
How to Implement LLM Grounding using Retrieval Augmented Generation Technique(RAG)

How to Implement LLM Grounding using Retrieval Augmented Generation Technique(RAG)

Comments
3 min read
Turn Your PDF Library into a Searchable Research Database (in ~100 Lines with CocoIndex)

Turn Your PDF Library into a Searchable Research Database (in ~100 Lines with CocoIndex)

5
Comments
4 min read
Fix Your AI Agent: Weekly Debugging AMA (RAG, Voice, Copilot, Text2SQL)

Fix Your AI Agent: Weekly Debugging AMA (RAG, Voice, Copilot, Text2SQL)

Comments
1 min read
Knowledge base in AI: why Q&A websites are a unique training asset

Knowledge base in AI: why Q&A websites are a unique training asset

Comments
4 min read
Building Production-Ready RAG in FastAPI with Vector Databases

Building Production-Ready RAG in FastAPI with Vector Databases

1
Comments
4 min read
Building Production RAG Systems in Days, Not Weeks: Introducing ShinRAG

Building Production RAG Systems in Days, Not Weeks: Introducing ShinRAG

Comments
4 min read
Building a 95% Precision Offline

Building a 95% Precision Offline

Comments
6 min read
From Static Docs to Living Knowledge: Building an STS‑Aware Retrieval‑Augmented Agent Backend

From Static Docs to Living Knowledge: Building an STS‑Aware Retrieval‑Augmented Agent Backend

Comments
4 min read
Flow Analysis for Voice Agents: Turning Debugging into an Engineering Task

Flow Analysis for Voice Agents: Turning Debugging into an Engineering Task

Comments
1 min read
RAG 2.0: Why Reranking Has Become the Core of Modern RAG Systems

RAG 2.0: Why Reranking Has Become the Core of Modern RAG Systems

1
Comments
4 min read
Can eval setup be automatically scaffolded?

Can eval setup be automatically scaffolded?

1
Comments 2
3 min read
How RAG Works...

How RAG Works...

3
Comments 2
2 min read
Understanding the logic behind 'Chat with PDF' apps by building a Retrieval-Augmented Generation agent manually.

Understanding the logic behind 'Chat with PDF' apps by building a Retrieval-Augmented Generation agent manually.

1
Comments
1 min read
loading...