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.
I built an open-source LLM eval platform with a ReAct agent that diagnoses quality regressions

I built an open-source LLM eval platform with a ReAct agent that diagnoses quality regressions

1
Comments
3 min read
How AI Apps Actually Use LLMs: Introducing RAG

How AI Apps Actually Use LLMs: Introducing RAG

7
Comments 5
4 min read
Stop stuffing tools into your agent 😤

Stop stuffing tools into your agent 😤

1
Comments
7 min read
I Built a RAG Pipeline. Then I Realized Retrieval Is the Real Model

I Built a RAG Pipeline. Then I Realized Retrieval Is the Real Model

3
Comments 2
3 min read
The Meeting Tax: Why Client Calls Steal 8–12 Hours/Week from Small-Agency AI Engineers (and How to Fix It)

The Meeting Tax: Why Client Calls Steal 8–12 Hours/Week from Small-Agency AI Engineers (and How to Fix It)

1
Comments
4 min read
Understanding RAG by Building a ChatPDF App with NumPy (Part 1)

Understanding RAG by Building a ChatPDF App with NumPy (Part 1)

1
Comments
3 min read
Same Model, Different Environment, Different Results

Interface design shaping model reasoning

Same Model, Different Environment, Different Results

5
Comments 10
9 min read
RAG Pipelines in Production: Vector Database Benchmarks, Chunking Strategies, and Hybrid Search Data

RAG Pipelines in Production: Vector Database Benchmarks, Chunking Strategies, and Hybrid Search Data

1
Comments 1
6 min read
ARKHEIN 0.1.0: The Great Decoupling

ARKHEIN 0.1.0: The Great Decoupling

Comments
3 min read
RAG in the Wild: What I Learned After Two Weeks of Chunking Experiments

RAG in the Wild: What I Learned After Two Weeks of Chunking Experiments

Comments 2
7 min read
Why Your RAG System Returns Garbage (And How to Actually Fix It)

Why Your RAG System Returns Garbage (And How to Actually Fix It)

Comments
5 min read
Build and deploy a RAG pipeline as a REST API in under 5 minutes with RAGLight

Build and deploy a RAG pipeline as a REST API in under 5 minutes with RAGLight

Comments
3 min read
Cache semántico y FAQ matching: cómo reduje un 40% el costo de LLM en mi motor RAG

Cache semántico y FAQ matching: cómo reduje un 40% el costo de LLM en mi motor RAG

Comments
8 min read
Fine-tuning vs RAG: Cuándo Usar Cada Enfoque para LLMs en Producción

Fine-tuning vs RAG: Cuándo Usar Cada Enfoque para LLMs en Producción

Comments
8 min read
Ask vs Act: RAG, Tool Use and AI agents

Ask vs Act: RAG, Tool Use and AI agents

2
Comments
4 min read
👋 Sign in for the ability to sort posts by relevant, latest, or top.