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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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Introducing NovaDesk AI: Building a Stateful Hotel Concierge with LangGraph

Introducing NovaDesk AI: Building a Stateful Hotel Concierge with LangGraph

BERJAYA BERJAYA BERJAYA 7
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3 min read
How to use LanceDB for RAG: hybrid search, one table per workspace, and the single-writer catch

How to use LanceDB for RAG: hybrid search, one table per workspace, and the single-writer catch

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7 min read
Digital Garden RAG - post DEV

Digital Garden RAG - post DEV

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2 min read
Rethinking Code Retrieval

Rethinking Code Retrieval

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5 min read
2026 Healthcare Appointment Retrieval — Fresh Chunks and Verifiable Citations

2026 Healthcare Appointment Retrieval — Fresh Chunks and Verifiable Citations

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7 min read
RAG Embeddings Admission — 3 Token Gates Before Batch Document Indexing

RAG Embeddings Admission — 3 Token Gates Before Batch Document Indexing

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7 min read
How Did the AI Era Take Shape? And Who Holds Its Keys?

How Did the AI Era Take Shape? And Who Holds Its Keys?

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28 min read
Your AI Application Is Only as Good as the Data Behind It

Your AI Application Is Only as Good as the Data Behind It

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3 min read
Why I Chose Retrieval Architecture: 3 Freshness Windows for a Restaurant Menu Assistant

Why I Chose Retrieval Architecture: 3 Freshness Windows for a Restaurant Menu Assistant

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4 min read
The one-word bug that kills keyword search in your Postgres RAG

The one-word bug that kills keyword search in your Postgres RAG

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3 min read
Catalog Change Budgets for Reliable E-commerce Retrieval and Freshness Control

Catalog Change Budgets for Reliable E-commerce Retrieval and Freshness Control

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7 min read
Why Fragmented Formulas Ruin Math RAG (and How to Extract Clean LaTeX Blocks from Technical PDFs)

Why Fragmented Formulas Ruin Math RAG (and How to Extract Clean LaTeX Blocks from Technical PDFs)

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5 min read
2026 Node.js Retrieval Contracts for Itinerary Planners (and Explicit Latency Caps)

2026 Node.js Retrieval Contracts for Itinerary Planners (and Explicit Latency Caps)

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5 min read
Restaurant Menu Retrieval Architecture: 4 Freshness Windows for Index Cost

Restaurant Menu Retrieval Architecture: 4 Freshness Windows for Index Cost

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6 min read
¿Cómo sabe Claude si una cita de una base de conocimiento de osFoundry sigue vigente?

¿Cómo sabe Claude si una cita de una base de conocimiento de osFoundry sigue vigente?

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8 min read
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