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.
Code Explanation: "Repomix: Codebase Packaging for AI Consumption"

Code Explanation: "Repomix: Codebase Packaging for AI Consumption"

Comments
5 min read
OpenAI Compatible API

OpenAI Compatible API

2
Comments
11 min read
How to Build a RAG Chatbot with LangChain, Milvus, Together AI Mixtral 8x7B Instruct v0.1, and OpenAI text-embedding-3-large

How to Build a RAG Chatbot with LangChain, Milvus, Together AI Mixtral 8x7B Instruct v0.1, and OpenAI text-embedding-3-large

Comments
8 min read
Understanding the Key Components of RAG: Retriever and Generator

Understanding the Key Components of RAG: Retriever and Generator

Comments
2 min read
DuckDB vs. ClickHouse Local: A Comparative Analysis for Analytical Workloads

DuckDB vs. ClickHouse Local: A Comparative Analysis for Analytical Workloads

Comments
4 min read
LangChain + FalkorDB: Building AI Agents with Memory

LangChain + FalkorDB: Building AI Agents with Memory

5
Comments 1
2 min read
The Future of AI: How Retrieval-Augmented Generation (RAG) is Changing the Game

The Future of AI: How Retrieval-Augmented Generation (RAG) is Changing the Game

Comments
3 min read
Langflow: Build Powerful AI Apps with Drag-and-Drop Simplicity

Langflow: Build Powerful AI Apps with Drag-and-Drop Simplicity

Comments
3 min read
Table Augmented Generation: Enhancing LLMs with Structured Data

Table Augmented Generation: Enhancing LLMs with Structured Data

1
Comments
5 min read
Weekly Updates - Feb 28, 2025

Weekly Updates - Feb 28, 2025

Comments
2 min read
Overview:"Agentic Retrieval-Augmented Generation: A Comprehensive Survey"

Overview:"Agentic Retrieval-Augmented Generation: A Comprehensive Survey"

Comments
8 min read
Deployable On-Premises RAG

Deployable On-Premises RAG

1
Comments
1 min read
Building an AI-Powered E-commerce Chatbot with LangChain and Gemini

Building an AI-Powered E-commerce Chatbot with LangChain and Gemini

Comments
1 min read
🚀 Announcing Rankify: The All-in-One Toolkit for Retrieval, Re-Ranking, and RAG

🚀 Announcing Rankify: The All-in-One Toolkit for Retrieval, Re-Ranking, and RAG

5
Comments
1 min read
Building Natural Language Command Interfaces: Tic-Tac-Toes with LLMs

Building Natural Language Command Interfaces: Tic-Tac-Toes with LLMs

Comments
4 min read
What is RAG and how Alibaba Cloud Elasticsearch enhances AI search with retrieval-augmented generation

What is RAG and how Alibaba Cloud Elasticsearch enhances AI search with retrieval-augmented generation

Comments
12 min read
Personalized Language Generation via Bayesian Metric Augmented Retrieval

Personalized Language Generation via Bayesian Metric Augmented Retrieval

Comments
2 min read
Deep Diving Into AI_devs 3: What I Learned And How You Can Benefit

Deep Diving Into AI_devs 3: What I Learned And How You Can Benefit

Comments
7 min read
Virtual Research Analyst - Harnessing Agentic and Multi-modal RAG

Virtual Research Analyst - Harnessing Agentic and Multi-modal RAG

Comments
1 min read
Building a Multi-modal Production RAG

Building a Multi-modal Production RAG

Comments
2 min read
How to Create RAG using DeepSeek R1, Ollama & Semantic Kernel .NET

How to Create RAG using DeepSeek R1, Ollama & Semantic Kernel .NET

3
Comments 2
4 min read
Let’s Build Enterprise Cybersecurity Risk Assessment Using AI Agents

Let’s Build Enterprise Cybersecurity Risk Assessment Using AI Agents

Comments
2 min read
RAG Chatbot: Build with LangChain, Milvus, Fireworks AI 🔥Llama 3.1 8B Instruct, and Cohere embed-multilingual-v2.0

RAG Chatbot: Build with LangChain, Milvus, Fireworks AI 🔥Llama 3.1 8B Instruct, and Cohere embed-multilingual-v2.0

2
Comments
8 min read
Building an IBM AIX Expert Chatbot using RAG and FAISS

Building an IBM AIX Expert Chatbot using RAG and FAISS

Comments
3 min read
RAG: What, Why and How

RAG: What, Why and How

Comments
6 min read
loading...