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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 Explained: How to Build AI Systems That Can Use Your Own Knowledge
taha_hussein_bb6f837d4c81 profile

RAG Explained: How to Build AI Systems That Can Use Your Own Knowledge

#ai #llm #rag
1
Comments
5 min read
# Building a Personal Notes Assistant with RAG, Amazon Bedrock, and Pinecone
d3vjamal profile

# Building a Personal Notes Assistant with RAG, Amazon Bedrock, and Pinecone

#ai #programming #rag #python
2
Comments
5 min read
About Best in IT: Practical AI, Automation and Developer Tools
aponiedzialek profile

About Best in IT: Practical AI, Automation and Developer Tools

#ai #llm #rag #programming
1
Comments
3 min read
Building a RAG Retrieval Service: pgvector, Embedding Migrations, and Provenance Tracking
humzakt profile

Building a RAG Retrieval Service: pgvector, Embedding Migrations, and Provenance Tracking

#rag #pgvector #embeddings #postgres
Comments
3 min read
LlamaIndex re-retrieves your chunks — and re-sends up to 75% of your context — on every chat turn
wartzarbee profile

LlamaIndex re-retrieves your chunks — and re-sends up to 75% of your context — on every chat turn

#ai #rag #python #llamaindex
Comments
3 min read
What the Hell Is JEV? And Why Does It Matter in 2027?
solitrix02 profile

What the Hell Is JEV? And Why Does It Matter in 2027?

#ai #programming #rag #machinelearning
5
Comments 1
8 min read
Your Documents, Chunked and Searchable: The Knowledge Base in ByteChef
bytechef profile

Your Documents, Chunked and Searchable: The Knowledge Base in ByteChef

#agents #ai #rag #search
Comments
7 min read
Building a Closed-Domain Agentic AI Knowledge Assistant with Hybrid RAG
sanjay_sajukumar_04 profile

Building a Closed-Domain Agentic AI Knowledge Assistant with Hybrid RAG

#ai #machinelearning #python #rag
2
Comments
2 min read
Enterprise RAG Without Per-Token Pricing: DeepSeek R1 on SageMaker with OpenSearch
jon_handler_9bb3e6b4a2fd0 profile

Enterprise RAG Without Per-Token Pricing: DeepSeek R1 on SageMaker with OpenSearch

#opensearch #rag #vectorsearch #airetrieval
1
Comments
5 min read
RAG always needs a dedicated vector database — challenged
letusai15 profile

RAG always needs a dedicated vector database — challenged

#rag #vectordatabase #llm #ai
6
Comments 1
1 min read
Architectural Bottlenecks and Mitigation Strategies in Production Grade RAG Systems
vkimutai profile

Architectural Bottlenecks and Mitigation Strategies in Production Grade RAG Systems

#architecture #llm #performance #rag
10
Comments 3
3 min read
I described 1,245 tables with an LLM and retrieval got worse
ashish_sinha_5241c7673d93 profile

I described 1,245 tables with an LLM and retrieval got worse

#ai #llm #rag #search
4
Comments 86
8 min read
Securing a RAG Pipeline — The Threats I Designed Against and the Ones I Didn't
pgmpofu profile

Securing a RAG Pipeline — The Threats I Designed Against and the Ones I Didn't

#security #rag #python #ai
1
Comments 1
8 min read
The Unit of Return: a 4-Arm Retrieval Experiment on a Real Codebase
mansio profile

The Unit of Return: a 4-Arm Retrieval Experiment on a Real Codebase

#ai #rag #python #architecture
12
Comments 2
12 min read
Local Embeddings vs. API Embeddings — Why I Chose sentence-transformers
pgmpofu profile

Local Embeddings vs. API Embeddings — Why I Chose sentence-transformers

#python #ai #rag #architecture
Comments 1
6 min read
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