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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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What happens when your RAG system retrieves the wrong documents?

What happens when your RAG system retrieves the wrong documents?

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2 min read
Where to cut a prompt so the cache actually hits

Where to cut a prompt so the cache actually hits

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

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

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3 min read
We Deleted Our Vector Database. Postgres Was Faster.

We Deleted Our Vector Database. Postgres Was Faster.

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9 min read
RAG vs MAG: Two Paths to Smarter AI Memory

RAG vs MAG: Two Paths to Smarter AI Memory

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

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

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

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

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

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

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15 min read
From Shredded Papers to the Grand Map Room: Why Knowledge Graphs are Revolutionizing RAG

From Shredded Papers to the Grand Map Room: Why Knowledge Graphs are Revolutionizing RAG

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3 min read
Vector Databases: The Foundation of Modern AI Applications

Vector Databases: The Foundation of Modern AI Applications

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13 min read
RAG vs Memory vs Tools: What Information Should an AI Agent Actually Store?

RAG vs Memory vs Tools: What Information Should an AI Agent Actually Store?

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16 min read
From Website URL to Useful AI Support Answers: A Practical Training Workflow

From Website URL to Useful AI Support Answers: A Practical Training Workflow

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4 min read
How to Build an AI PDF Analyzer with MongoDB, RAG, and Ollama

How to Build an AI PDF Analyzer with MongoDB, RAG, and Ollama

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30 min read
Your LLM Trace Is Green. Why Is the RAG Answer Still Wrong?

Your LLM Trace Is Green. Why Is the RAG Answer Still Wrong?

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6 min read
FHIR R4 for ML Engineers: What Actually Lives in a Patient Record

FHIR R4 for ML Engineers: What Actually Lives in a Patient Record

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6 min read
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