But don't let the model check itself The post Design Loops, Not Prompts appeared first on Towards Data Science .
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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).
The article titled "Put Your Own Logic Inside the Codex Agentic Loop" discusses how to incorporate custom logic into Codex hooks, as described in the post on Towards Data Science.
Small prompt changes can silently break critical behavior in production. This article introduces a practical framework to detect hidden regressions before users notice.
Enterprise Document Intelligence [Vol. 1 #6quinquies] - Prompt engineering, then context engineering, then loop engineering.
The article describes how the author constructed a prompt dependency graph to identify which prompts are affected when a single prompt changes. By separating all reachable components from the smaller subset that truly requires evaluation, the graph helps focus retesting efforts. This approach streamlines testing by pinpointing only the prompts that need targeted evaluation.
Learn about the concept of loops to power your coding agents. The post How to Create Powerful Loops in Claude Code appeared first on Towards Data Science .