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Retrieval augmented generation, or RAG, is an architectural approach that can improve the efficacy of large language model (LLM) applications by leveraging custom data.

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RAG Has a Routing Problem: Knowing Which Knowledge Source to Trust
hosseinhezami profile

RAG Has a Routing Problem: Knowing Which Knowledge Source to Trust

#ai #rag #knowledge #tutorial
7
Comments
13 min read
Dify vs Custom Development: When Visual AI Workflows Actually Work (and When They Don't)
zediot profile
ZedIoT

Dify vs Custom Development: When Visual AI Workflows Actually Work (and When They Don't)

#ai #architecture #backend #rag
Comments
6 min read
End-to-End Enterprise RAG Pipeline with IBM Docling Pipelines and OpenSearch
aairom profile

End-to-End Enterprise RAG Pipeline with IBM Docling Pipelines and OpenSearch

#docling #opensearch #rag #bob
1
Comments
7 min read
PDF RAG Summarization Runbook: Semantic Search, Embeddings, Rerank, Final Summary
sebastiancole3681 profile

PDF RAG Summarization Runbook: Semantic Search, Embeddings, Rerank, Final Summary

#rag #embeddings #go
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7 min read
MCP vs RAG: Two AI Buzzwords, Explained Without the Buzzwords
karamkhoury88 profile

MCP vs RAG: Two AI Buzzwords, Explained Without the Buzzwords

#ai #rag #mcp #architecture
1
Comments
4 min read
How AI Crawlers Read Your Website: Preparing Content for LLMs
pankti profile
Pankti

How AI Crawlers Read Your Website: Preparing Content for LLMs

#ai #webdev #llm #rag
1
Comments
2 min read
RAG Retrieval Can Look Relevant and Still Be Wrong
thequery_addy profile

RAG Retrieval Can Look Relevant and Still Be Wrong

#rag
Comments 1
2 min read
What happens when your RAG system retrieves the wrong documents?
101_theory profile
Niko

What happens when your RAG system retrieves the wrong documents?

#ai #agents #programming #rag
1
Comments 2
2 min read
Where to cut a prompt so the cache actually hits
gsatya147 profile

Where to cut a prompt so the cache actually hits

#rag #llm #python #infrastructure
Comments
4 min read
Multi-hop questions break vector search. Here is a graph layer for Qdrant that fixes them.
demigoddsk profile

Multi-hop questions break vector search. Here is a graph layer for Qdrant that fixes them.

#rag #qdrant #python #ai
Comments
3 min read
We Deleted Our Vector Database. Postgres Was Faster.
infoinlet1 profile

We Deleted Our Vector Database. Postgres Was Faster.

#ai #postgres #database #rag
11
Comments 3
9 min read
RAG vs MAG: Two Paths to Smarter AI Memory
saumya_ranjanmohapatra_3 profile

RAG vs MAG: Two Paths to Smarter AI Memory

#ai #machinelearning #rag #llm
Comments
3 min read
Local-first multi-hop RAG: Chroma + an entity graph, zero tokens per query
demigoddsk profile

Local-first multi-hop RAG: Chroma + an entity graph, zero tokens per query

#rag #python #ai #opensource
Comments
2 min read
Vector RAG can't fix long-context state tracking (33 runs, zero variance)
he_fangsheng_23597e192e87 profile

Vector RAG can't fix long-context state tracking (33 runs, zero variance)

#ai #llm #rag
1
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2 min read
I Rebuilt My RAG Pipeline Without LangChain — What Got Better and What Got Worse
hosseinhezami profile

I Rebuilt My RAG Pipeline Without LangChain — What Got Better and What Got Worse

#ai #rag #langchain #pipeline
9
Comments 5
15 min read
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