--- title: Loop Engineering for RAG: The Small Loops Inside Each... description: Part of a series building an enterprise RAG system, this piece defines 'loop engineering' as the discipline of deciding when a pipeline retries a failed step,... canonical: https://daily.dev/posts/loop-engineering-for-rag-the-small-loops-inside-each-step-the-big-loops-across-the-pipeline-ugioi9xao twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: Loop Engineering for RAG: The Small Loops Inside Each Step, the Big Loops Across the Pipeline | daily.dev og:description: Part of a series building an enterprise RAG system, this piece defines 'loop engineering' as the discipline of deciding when a pipeline retries a failed step,... og:url: https://daily.dev/posts/loop-engineering-for-rag-the-small-loops-inside-each-step-the-big-loops-across-the-pipeline-ugioi9xao og:image: https://api.daily.dev/og/posts/UgIoi9XAo.png og:image:alt: Loop Engineering for RAG: The Small Loops Inside Each Step, the Big Loops Across the Pipeline og:image:width: 1200 og:image:height: 630 og:locale: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # Loop Engineering for RAG: The Small Loops Inside Each Step, the Big Loops Across the Pipeline **[Towards Data Science](https://daily.dev/sources/tds)** · 15 min read · 0 upvotes · 0 comments ## Summary Part of a series building an enterprise RAG system, this piece defines 'loop engineering' as the discipline of deciding when a pipeline retries a failed step, when it stops, and how it recovers. It breaks loops into three control surfaces (trigger, termination, recovery), distinguishes small loops inside a single processing brick from big loops that cross bricks (e.g., generation flagging incomplete retrieval), and lays out an anti-spinning rule: a retry is only legitimate if the payload, model, or strategy changes between attempts. It also covers common failure modes (infinite retries on transient errors, spinning on deterministic schema failures, ignored confidence flags) and frames three nested loops at different cadences: the dispatcher's inner loop, an engineer's dev loop, and a human expert's outer loop, borrowing from Andrew Ng's 'Three Key Loops' framing. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Questions this post answers ### What is the difference between prompt engineering, context engineering, and loop engineering in an LLM pipeline? Prompt engineering writes the call itself (system message, user message, schema), context engineering chooses what enters and exits the model's context window between calls, and loop engineering decides when the next call happens, what triggers it, when it stops, and how the system recovers from failure. These three disciplines stack on top of a single LLM call to form the full agent stack. _daily.dev surfaces deep dives like this for engineers layering retries and context management on LLM calls._ ### How do you prevent an LLM retry loop from spinning on the same error without making progress? A retry loop should only fire again when something concrete has changed since the previous attempt: the payload (e.g., widened retrieval scope or an added prompt constraint), the model (escalating from a small to a large model), or the strategy (switching from keyword to dense retrieval). If a retry carries no such change, it should not fire; bounding retries to a small number, such as six, and logging a changed-since-last field on every iteration catches spinning loops before they burn through API budget. _solving retry logic in your own pipeline gets easier with practical patterns curated on daily.dev.ic bugs.strategies._ ### What retry schedule should an LLM API wrapper use for transient failures like timeouts and rate limits? A common pattern uses retry-with-backoff with six attempts and delays of 2, 4, 8, 16, 32, and 60 seconds plus jitter, applied to a fixed list of retriable exceptions such as APITimeoutError, APIConnectionError, RateLimitError, 429, 500, 502, 503, and 504. After the final attempt fails, the wrapper raises a hard error rather than retrying indefinitely, preventing runaway API costs. _engineers hardening LLM API calls against timeouts and rate limits track patterns like this on daily.dev._ ## Similar posts on daily.dev - [Loop Engineering, Clearly Explained\!](https://daily.dev/posts/loop-engineering-clearly-explained--mt3qmkm25) · Daily Dose of Data Science \| Avi Chawla \| Substack · 3 upvotes · 0 comments - [An Introduction to Loop Engineering](https://daily.dev/posts/an-introduction-to-loop-engineering-7ctzwxfxz) · Machine Learning Mastery · 2 upvotes · 0 comments - [Loop Engineering for RAG Question Parsing: The Small Loop That Runs Before Retrieval](https://daily.dev/posts/loop-engineering-for-rag-question-parsing-the-small-loop-that-runs-before-retrieval-ez3bh9lwh) · Towards Data Science · 1 upvotes · 0 comments - [Loop Engineering](https://daily.dev/posts/loop-engineering-h663rbtdz) · Elevate · 3 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) [View this post on daily.dev](https://daily.dev/posts/loop-engineering-for-rag-the-small-loops-inside-each-step-the-big-loops-across-the-pipeline-ugioi9xao) ```json 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