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# Prompt Engineering & Loop Engineering, Clearly Explained!

**[Daily Dose of Data Science \| Avi Chawla \| Substack](https://daily.dev/sources/dailydoseofds)** · 7 min read · 1 upvotes · 0 comments

## Summary

A newsletter covering two main topics: the concept of 'loop engineering' for AI agents and a hands-on Corrective RAG (CRAG) demo. The loop engineering section explains how agents operate as inner while-loops (the ReAct pattern) and discusses the emerging practice of automating the outer loop — removing the human from the iteration cycle — along with real tradeoffs like loss of system understanding, context growth degrading model quality, the need for deterministic stop conditions, and rising costs. The CRAG demo shows a self-assessing RAG workflow built with LlamaIndex, Milvus, Firecrawl, and CometML Opik that falls back to web search when retrieved documents are deemed irrelevant.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://blog.dailydoseofds.com/p/prompt-engineering-and-loop-engineering>

## Similar posts on daily.dev

- [An Introduction to Loop Engineering](https://daily.dev/posts/an-introduction-to-loop-engineering-7ctzwxfxz) · Machine Learning Mastery · 2 upvotes · 0 comments

---

Tags: [#llm](https://daily.dev/tags/llm), [#ai-agents](https://daily.dev/tags/ai-agents), [#rag](https://daily.dev/tags/rag), [#prompt-engineering](https://daily.dev/tags/prompt-engineering), [#llama](https://daily.dev/tags/llama)

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