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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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Why Organizations Forget Even When Nothing Is Deleted

Why Organizations Forget Even When Nothing Is Deleted

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Comments 22
2 min read
Your baseline scored 0.000. That's a broken harness, not a result.

Your baseline scored 0.000. That's a broken harness, not a result.

Comments 3
4 min read
I Was Optimizing Ranking While the Real Problem Was Selection

I Was Optimizing Ranking While the Real Problem Was Selection

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Comments 4
2 min read
Qdrant vs Pinecone: Self-Hosted Vector Search for Production RAG

Qdrant vs Pinecone: Self-Hosted Vector Search for Production RAG

Comments 3
11 min read
How Japan’s Research Labs Are Building RAG Systems That Actually Work — And What Western Teams Keep Getting Wrong

How Japan’s Research Labs Are Building RAG Systems That Actually Work — And What Western Teams Keep Getting Wrong

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Comments
4 min read
Replacing Cross-Encoder Reranking with a Weighted Hybrid Score

Replacing Cross-Encoder Reranking with a Weighted Hybrid Score

Comments
5 min read
1st post

1st post

Comments 1
1 min read
AI Agent Orchestration: Proxmox Automation, OpenAI Data Agents & Azure Serverless Runtime

AI Agent Orchestration: Proxmox Automation, OpenAI Data Agents & Azure Serverless Runtime

Comments
3 min read
Phase 4: Retrieval Quality & Grounded Answers

Why closest matches aren't always relevant

Phase 4: Retrieval Quality & Grounded Answers

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Comments 10
12 min read
Why Most RAG Systems Fail in Production: The Hidden Architecture Problems Behind AI Search

Why Most RAG Systems Fail in Production: The Hidden Architecture Problems Behind AI Search

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Comments 5
12 min read
Your RAG Retrieved the Right Documents but Still Gave the Wrong Answer

Your RAG Retrieved the Right Documents but Still Gave the Wrong Answer

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2 min read
Building an Enterprise RAG Knowledge Assistant: Lessons from Production

Building an Enterprise RAG Knowledge Assistant: Lessons from Production

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4 min read
Day 1/60: Building My First RAG Agent for Developers

Day 1/60: Building My First RAG Agent for Developers

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1 min read
Reciprocal Rank Fusion (RRF): how it works and when to skip it

Reciprocal Rank Fusion (RRF): how it works and when to skip it

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Comments 1
12 min read
The Model Does Not Need Memory. The Situation Does.

Value of information absent from weights

The Model Does Not Need Memory. The Situation Does.

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Comments 38
11 min read
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