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#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.

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The Next RAG Problem Isn’t Retrieval — It’s Knowing When Not to Retrieve
hosseinhezami profile

The Next RAG Problem Isn’t Retrieval — It’s Knowing When Not to Retrieve

#rag #ai #agents #problem
5
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13 min read
Building a Production RAG Pipeline with n8n, Qdrant, and Gemini: A Step-by-Step Walkthrough
hosseinhezami profile

Building a Production RAG Pipeline with n8n, Qdrant, and Gemini: A Step-by-Step Walkthrough

#ai #rag #n8n #gemini
5
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15 min read
RAG vs fine-tuning: which one your problem actually needs
pranjulrathour profile

RAG vs fine-tuning: which one your problem actually needs

#rag #finetuning #architecture #decisionguide
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3 min read
Query rewriting for RAG: expansion, decomposition and HyDE, explained simply
pranjulrathour profile

Query rewriting for RAG: expansion, decomposition and HyDE, explained simply

#rag #retrieval #queryrewriting #hyde
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3 min read
Citations in RAG answers: how to make every sentence traceable to a source
pranjulrathour profile

Citations in RAG answers: how to make every sentence traceable to a source

#rag #citations #trust #guide
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3 min read
Seven ways RAG fails in production, and the fix for each
pranjulrathour profile

Seven ways RAG fails in production, and the fix for each

#rag #production #debugging #guide
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3 min read
How to evaluate a RAG system: recall, faithfulness and the questions that matter
pranjulrathour profile

How to evaluate a RAG system: recall, faithfulness and the questions that matter

#rag #evaluation #metrics #guide
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3 min read
FAISS vs Qdrant vs pgvector: choosing a vector store for a student or startup RAG project
pranjulrathour profile

FAISS vs Qdrant vs pgvector: choosing a vector store for a student or startup RAG project

#rag #vectordatabase #faiss #qdrant
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3 min read
Multi-provider LLM fallback: staying up when one API goes down
pranjulrathour profile

Multi-provider LLM fallback: staying up when one API goes down

#llmapps #reliability #architecture #rag
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3 min read
A RAG project for your college: notes, syllabus and PYQs you can actually query
pranjulrathour profile

A RAG project for your college: notes, syllabus and PYQs you can actually query

#rag #studentproject #portfolio #college
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3 min read
Choosing an embedding model for RAG (and when to switch)
pranjulrathour profile

Choosing an embedding model for RAG (and when to switch)

#rag #embeddings #retrieval #guide
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3 min read
Reranking in RAG: why a cross-encoder second pass fixes most bad answers
pranjulrathour profile

Reranking in RAG: why a cross-encoder second pass fixes most bad answers

#rag #reranking #crossencoder #retrieval
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3 min read
Chunking strategies for RAG: sizes, overlap and structure-aware splits
pranjulrathour profile

Chunking strategies for RAG: sizes, overlap and structure-aware splits

#rag #chunking #retrieval #guide
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3 min read
How to add UI for your RAG?!?
coji831 profile

How to add UI for your RAG?!?

#ai #fastapi #react #rag
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1 min read
RAG Classifications, Architectures: A Field Guide for Production-Grade Systems
sreeraj-sreenivasan profile

RAG Classifications, Architectures: A Field Guide for Production-Grade Systems

#ai #python #rag #architecture
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8 min read
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