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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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When Good RAG Systems Fail (And How Production Teams Prevent It)

When Good RAG Systems Fail (And How Production Teams Prevent It)

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9 min read
Production RAG at Scale: Architecture Patterns for 1M+ Documents

Production RAG at Scale: Architecture Patterns for 1M+ Documents

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4 min read
Building a Document-RAG Agent on GCP's Agent Development Kit (ADK)

Building a Document-RAG Agent on GCP's Agent Development Kit (ADK)

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14 min read
Building RAG that doesn't hallucinate

Building RAG that doesn't hallucinate

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4 min read
Where Does RAG Actually Cost You Money? I Decided to Stop Guessing.

Debunks the myth that embeddings drive up bills

Where Does RAG Actually Cost You Money? I Decided to Stop Guessing.

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4 min read
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.

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

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

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11 min read
RAG Chunking Strategy: How to Preserve Context Instead of Destroying It

RAG Chunking Strategy: How to Preserve Context Instead of Destroying It

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12 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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4 min read
Replacing Cross-Encoder Reranking with a Weighted Hybrid Score

Replacing Cross-Encoder Reranking with a Weighted Hybrid Score

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5 min read
1st post

1st post

Comments 1
1 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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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
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