Towards Data Science August 3, 2026 By angela shi

Prompt, Context, Loop: The Three Engineering Layers Every RAG System Is Built On

Read the original on Towards Data Science →

Enterprise Document Intelligence [Vol. 1 #M2] - Every RAG system is built in three engineering layers stacked on one LLM call: prompt (the call itself), context (what fills the model’s window), loop (when the next call fires and when it stops).

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at Towards Data Science.

Towards Data Science

Aug 14

RAG Workflow and Loop Engineering: The Dispatcher That Decides When to Loop and When to Stop

Enterprise Document Intelligence [Vol. 1 #13] - Putting the patterns together, and why this is what “agentic RAG” should look like The post RAG Workflow and Loop Engineering: The Dispatcher That Decides When to Loop and When to Stop appeared first on Towards Data Science .

By angela shi

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Loop Engineering for RAG: The Small Loops Inside Each Step, the Big Loops Across the Pipeline

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Loop Engineering for RAG Question Parsing: The Small Loop That Runs Before Retrieval

Enterprise Document Intelligence [Vol. 1 #6quinquies] - Prompt engineering, then context engineering, then loop engineering.

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Towards Data Science

Jun 30

Context Engineering for RAG: The Four Typed Inputs Behind Every Answer

Enterprise Document Intelligence [Vol. 1 #7bis] - Tobi Lütke and Andrej Karpathy named the practice in 2025.

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Towards Data Science

Jun 30

Context Engineering for RAG: The Four Typed Inputs Behind Every RAG Answer

Enterprise Document Intelligence [Vol. 1 #7bis] - Tobi Lütke and Andrej Karpathy named the practice in 2025.

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Towards Data Science

Jun 30

Context Engineering for RAG : The Four Typed Inputs Behind Every RAG Answer

Enterprise Document Intelligence [Vol. 1 #7bis] - Tobi Lütke and Andrej Karpathy named the practice in 2025.

By Kezhan Shi

llms rag

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RAG Workflow and Loop Engineering: The Dispatcher That Decides When to Loop and When to Stop

Loop Engineering for RAG: The Small Loops Inside Each Step, the Big Loops Across the Pipeline

Loop Engineering for RAG Question Parsing: The Small Loop That Runs Before Retrieval

Context Engineering for RAG: The Four Typed Inputs Behind Every Answer

Context Engineering for RAG: The Four Typed Inputs Behind Every RAG Answer

Context Engineering for RAG : The Four Typed Inputs Behind Every RAG Answer