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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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RAG in production: the failure modes nobody warns you about

RAG in production: the failure modes nobody warns you about

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3 min read
When AI Is Confidently Wrong, Who's Responsible?

When AI Is Confidently Wrong, Who's Responsible?

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3 min read
Building Digester: A Cloud-Native Knowledge Concierge

Building Digester: A Cloud-Native Knowledge Concierge

2 min read
Stop Sending the Raw User Prompt Straight to Your Retriever

Stop Sending the Raw User Prompt Straight to Your Retriever

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2 min read
Phase 1: Document Ingestion - The Hidden Complexity Before Embeddings

Phase 1: Document Ingestion - The Hidden Complexity Before Embeddings

20 min read
How to Evolve a Linear LangChain RAG Pipeline into a Stateful, Multi-Agent Consensus Architecture

How to Evolve a Linear LangChain RAG Pipeline into a Stateful, Multi-Agent Consensus Architecture

1 min read
HyDE: Search With a Hypothetical Answer, Not the Question

HyDE: Search With a Hypothetical Answer, Not the Question

1 min read
How to make an AI research agent label facts vs inferences — a deterministic provenance pipeline

How to make an AI research agent label facts vs inferences — a deterministic provenance pipeline

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4 min read
Building RAG that doesn't hallucinate

Building RAG that doesn't hallucinate

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4 min read
Self-RAG: Let the Model Decide When to Retrieve, Then Grade Itself

Self-RAG: Let the Model Decide When to Retrieve, Then Grade Itself

1 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

5 min read
1st post

1st post

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1 min read
Fine-tuning vs RAG: Two Ways to Teach an LLM

Fine-tuning vs RAG: Two Ways to Teach an LLM

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

3 min read
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