---
title: "Discussion on \"The Anatomy of Production-Grade Retrieval\" | Hashnode"
description: "Discussion on \"The Anatomy of Production-Grade Retrieval\". Retrieval-Augmented Generation is often summarized as a three-step workflow: split documents, embed the chunks, and send the nearest results to a language model. That description is useful for a proto"
url: "https://hashnode.com/posts/the-anatomy-of-production-grade-retrieval/6a5ef376e6fedc609ed8211b"
---

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# [The Anatomy of Production-Grade Retrieval](https://ahmetozel.hashnode.dev/the-anatomy-of-production-grade-retrieval?utm_source=hashnode&utm_medium=feed)

Retrieval-Augmented Generation is often summarized as a three-step workflow: split documents, embed the chunks, and send the nearest results to a language model. That description is useful for a proto

Jul 21 · 34 min read · ahmetozel.hashnode.dev  
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## Written by

[Ahmet Özel](https://hashnode.com/@ahmetozel)

@ahmetozel · Istanbul, Turkey

  
[View profile](https://hashnode.com/@ahmetozel)

AI Engineer. Computer Vision, RAG and LLM agents.

AI Engineer focused on computer vision (YOLO/ONNX), RAG, and LLM agents. I build and ship production AI systems and write about what I learn.

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