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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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Hybrid Search and Reranking: What Each One Actually Buys You

Hybrid Search and Reranking: What Each One Actually Buys You

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13 min read
How to Build a Robust RAG System: Lessons from a Live Production Architecture

How to Build a Robust RAG System: Lessons from a Live Production Architecture

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8 min read
RAG is a search problem wearing a costume

RAG is a search problem wearing a costume

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8 min read
You do not need a vector database for your first RAG chatbot

You do not need a vector database for your first RAG chatbot

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5 min read
Power Up Your Agents: Execution Hooks and Smart Memory in Agent Kernel

Power Up Your Agents: Execution Hooks and Smart Memory in Agent Kernel

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6 min read
How Far Can Document Grounding Go Using the Document Itself?

How Far Can Document Grounding Go Using the Document Itself?

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1 min read
My PDF converter returned 200 OK and 214 characters for a 46-page contract — scanned pages have no text layer

My PDF converter returned 200 OK and 214 characters for a 46-page contract — scanned pages have no text layer

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2 min read
PDF to markdown for LLMs

PDF to markdown for LLMs

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8 min read
How I Tune RAG Pipelines with RAG-LCC: A Hands-On Local Guide

How I Tune RAG Pipelines with RAG-LCC: A Hands-On Local Guide

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7 min read
Pinecone vs Qdrant vs pgvector: The Same RAG Query in All Three (Python Code Comparison)

Pinecone vs Qdrant vs pgvector: The Same RAG Query in All Three (Python Code Comparison)

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2 min read
We removed 98.77% of an LLM’s input. Accuracy went up.

We removed 98.77% of an LLM’s input. Accuracy went up.

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3 min read
Exa vs Tavily in 2026: Our Agent Web Search Benchmark

Exa vs Tavily in 2026: Our Agent Web Search Benchmark

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11 min read
We Find the Public Files Others Don't: Building Archive777 to Research 1.4+ Million Source Files

We Find the Public Files Others Don't: Building Archive777 to Research 1.4+ Million Source Files

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4 min read
How AI Understands Meaning — Embeddings Explained for Backend Engineers

How AI Understands Meaning — Embeddings Explained for Backend Engineers

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8 min read
Don't Switch Embedding Models on Vibes. Build a Tiny Retrieval Eval in TypeScript.

Don't Switch Embedding Models on Vibes. Build a Tiny Retrieval Eval in TypeScript.

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10 min read
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