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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 RAG in LangChain

Hybrid RAG in LangChain

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7 min read
Agentic RAG in LangChain

Agentic RAG in LangChain

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6 min read
2-Step RAG in Langchain

2-Step RAG in Langchain

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5 min read
Your hallucination checker only sees the final paragraph

Your hallucination checker only sees the final paragraph

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3 min read
Building an AI-Powered Personalized Information Retrieval System

Building an AI-Powered Personalized Information Retrieval System

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3 min read
Is Your Knowledge Base Actually Thinking, or Just Retrieving?

Is Your Knowledge Base Actually Thinking, or Just Retrieving?

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6 min read
Building Dev-Code: An Agentic AI Coding Assistant With RAG Memory and VS Code Integration

Building Dev-Code: An Agentic AI Coding Assistant With RAG Memory and VS Code Integration

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4 min read
RAG for developers who aren't AI engineers: what actually matters

RAG for developers who aren't AI engineers: what actually matters

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8 min read
I Built a Personal AI That Actually Knows My Projects (RAG + Ollama, Zero Cloud)

I Built a Personal AI That Actually Knows My Projects (RAG + Ollama, Zero Cloud)

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5 min read
Chat with Your Documents: Building a RAG Pipeline with AWS Blocks

Chat with Your Documents: Building a RAG Pipeline with AWS Blocks

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16 min read
I open-sourced a macro execution layer to reduce coding-agent turns (60-task benchmark)

I open-sourced a macro execution layer to reduce coding-agent turns (60-task benchmark)

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1 min read
RAG - Semantic Caching

RAG - Semantic Caching

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3 min read
Leonard Shelby Is a RAG Pipeline

Leonard Shelby Is a RAG Pipeline

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5 min read
Try to Break Our AI Memory Benchmark

Try to Break Our AI Memory Benchmark

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2 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
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