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

đź‘‹ Sign in for the ability to sort posts by relevant, latest, or top.
AI for Knowledge Management: Real Workflows That Hold Up

AI for Knowledge Management: Real Workflows That Hold Up

Comments
8 min read
Building a RAG System in Rust with Qdrant, Rig, and gRPC 🦀

Building a RAG System in Rust with Qdrant, Rig, and gRPC 🦀

2
Comments 3
21 min read
Stop Caching the Whole LLM Response. Cache the Embedding.

Stop Caching the Whole LLM Response. Cache the Embedding.

Comments
8 min read
Your RAG Eval Set Is Probably Wrong. The Test That Catches It.

Your RAG Eval Set Is Probably Wrong. The Test That Catches It.

Comments
7 min read
Hybrid Search Is the Phrase You'll Hear at Every RAG Talk in 2026

Hybrid Search Is the Phrase You'll Hear at Every RAG Talk in 2026

Comments
7 min read
Why Every RAG Company Is Quietly Building a Graph Layer in 2026

Why Every RAG Company Is Quietly Building a Graph Layer in 2026

Comments
8 min read
Bringing MongoDB Atlas and Voyage AI to Dify: Build RAG Workflows and Data Agents Without Heavy Glue Code

Bringing MongoDB Atlas and Voyage AI to Dify: Build RAG Workflows and Data Agents Without Heavy Glue Code

Comments 2
8 min read
Cloudflare Boosts AI Agent Governance; Claude Model Choice & Advanced NLP

Cloudflare Boosts AI Agent Governance; Claude Model Choice & Advanced NLP

Comments
3 min read
What I Got Wrong Building a RAG Pipeline from Scratch in TypeScript

What I Got Wrong Building a RAG Pipeline from Scratch in TypeScript

2
Comments
9 min read
PolicyMind AI: Intelligent Insurance Document Assistant using Gemma 4

Gemma 4 Challenge: Build With Gemma 4 Submission

PolicyMind AI: Intelligent Insurance Document Assistant using Gemma 4

1
Comments
4 min read
5 RAG Failure Modes Nobody Warns You About in the Tutorials

5 RAG Failure Modes Nobody Warns You About in the Tutorials

Comments
7 min read
Postgres With pgvector vs Pinecone: 1 Million Embeddings, One Honest Comparison

Postgres With pgvector vs Pinecone: 1 Million Embeddings, One Honest Comparison

Comments
8 min read
RAG vs. Fine-Tuning vs. Prompting: 2026 Strategic Guide

RAG vs. Fine-Tuning vs. Prompting: 2026 Strategic Guide

Comments
4 min read
How to Handle Proprietary Jargon in LLM-as-a-Judge Evaluations

How to Handle Proprietary Jargon in LLM-as-a-Judge Evaluations

Comments
4 min read
I Built a Local-First AI Desktop Knowledge Base — Here's What I Learned

I Built a Local-First AI Desktop Knowledge Base — Here's What I Learned

Comments 2
6 min read
đź‘‹ Sign in for the ability to sort posts by relevant, latest, or top.