But don't let the model check itself The post Design Loops, Not Prompts appeared first 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).
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 .
Five scikit-learn defaults that deserve a closer look before your next model reaches production The post Your AI Assistant Wrote the Code. Who Checked the Defaults? appeared first on Towards Data Scie...
Enterprise Document Intelligence [Vol. 1 #8quater] - Two angles on the cascade, cost and a validation loop, backed by a real sweep of twenty local models against a hosted flagship The post Loop Engineering for RAG Generation: An LLM Cascade from a Cheap Local Model Up to a Hosted Flagship appeared first on Towards Data Science .
Prompt engineering helps you write better prompts—but it doesn’t help you change them safely. This article explores a common production failure where a simple variable rename breaks every live call, and introduces a lightweight static analysis tool that treats prompts like contracts, catching breaking changes before they ship.
Maximize your efficiency with Claude Code The post How to Efficiently Prompt Claude Code appeared first on Towards Data Science .
Enterprise Document Intelligence [Vol. 1 #8C] - Structured output is the start of validation, not the end: check the evidence, accept not-found, loop the feedback The post Validating the RAG Answer Before the User Sees It: Spans, Quotes, and the Feedback Loop appeared first on Towards Data Science .
Enterprise Document Intelligence [Vol. 1 #13bis] - The four bricks return useful results most of the time.