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.
How Generative AI is Revolutionizing Financial Institutions in 2025: Top 10 Use Cases

How Generative AI is Revolutionizing Financial Institutions in 2025: Top 10 Use Cases

Comments
7 min read
DO NOT use these LLM Metrics â›” And what to do instead!

DO NOT use these LLM Metrics â›” And what to do instead!

6
Comments
1 min read
Agentic Reasoning: How AI Models Use Tools to Solve Complex Problems

Agentic Reasoning: How AI Models Use Tools to Solve Complex Problems

1
Comments
3 min read
Building a Simple RAG System in Spring Boot with Ollama

Building a Simple RAG System in Spring Boot with Ollama

Comments
1 min read
Create Your Own AI Assistant, Coco AI v0.1.0 Released

Create Your Own AI Assistant, Coco AI v0.1.0 Released

Comments
2 min read
Connect external data (RAG) to AI agent in minutes

Connect external data (RAG) to AI agent in minutes

Comments
2 min read
Data Preparation Toolkit

Data Preparation Toolkit

Comments
1 min read
LLM Distillation: Optimizing Large Language Models for Efficiency

LLM Distillation: Optimizing Large Language Models for Efficiency

Comments
3 min read
Building Smart AI Agents: Designing a Multi-Functional RAG System

Building Smart AI Agents: Designing a Multi-Functional RAG System

Comments
3 min read
Build RAG Chatbot with LangChain, Milvus, Anthropic Claude 3 Opus, and OpenAI text-embedding-3-small

Build RAG Chatbot with LangChain, Milvus, Anthropic Claude 3 Opus, and OpenAI text-embedding-3-small

2
Comments
8 min read
The Evolution of Knowledge Work: A Comprehensive Guide to Agentic Retrieval-Augmented Generation (RAG)

The Evolution of Knowledge Work: A Comprehensive Guide to Agentic Retrieval-Augmented Generation (RAG)

Comments
2 min read
Alternativa a Bedrock Knowledge Base

Alternativa a Bedrock Knowledge Base

Comments
3 min read
Leveraging AI/ML for Finance and Trading: A Journey from ML Models to a 23% Gain

Leveraging AI/ML for Finance and Trading: A Journey from ML Models to a 23% Gain

Comments
5 min read
Error Analysis 🔧 Stop Guessing, Start Fixing AI Models

Error Analysis 🔧 Stop Guessing, Start Fixing AI Models

14
Comments
2 min read
My Building Of Trading Order Management System Using AI Agents

My Building Of Trading Order Management System Using AI Agents

1
Comments
2 min read
[Guide] Deploying Chainlit with RAG on Upsun 🚀

[Guide] Deploying Chainlit with RAG on Upsun 🚀

2
Comments
1 min read
Alibaba Cloud AI Search Solution Explained: Intelligent Search Driven by Large Language Models, Helping Enterprises in Digital

Alibaba Cloud AI Search Solution Explained: Intelligent Search Driven by Large Language Models, Helping Enterprises in Digital

Comments
9 min read
Enhancing Developer Productivity with Cursor's External Documentation Integration

Enhancing Developer Productivity with Cursor's External Documentation Integration

Comments
3 min read
Mastering Text-to-SQL with LLM Solutions and Overcoming Challenges

Mastering Text-to-SQL with LLM Solutions and Overcoming Challenges

Comments
7 min read
Gen AI Learnings : Hallucinations and your options

Gen AI Learnings : Hallucinations and your options

Comments
3 min read
A multi-head classifier using SetFit for query preprocessing

A multi-head classifier using SetFit for query preprocessing

Comments
1 min read
Semantic Similarity for Personal Knowledge Management

Semantic Similarity for Personal Knowledge Management

Comments
4 min read
AI Agent Memory: A Comparative Analysis of LangGraph, CrewAI, and AutoGen

AI Agent Memory: A Comparative Analysis of LangGraph, CrewAI, and AutoGen

1
Comments
6 min read
All about Function Calling in LLMS

All about Function Calling in LLMS

34
Comments 2
8 min read
Understanding RAGAS: A Comprehensive Framework for RAG System Evaluation

Understanding RAGAS: A Comprehensive Framework for RAG System Evaluation

Comments
4 min read
loading...