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).
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.
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 .
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.
Enterprise Document Intelligence [Vol. 1 #12] - The category of question most RAG pipelines silently fail on, and the pipeline shape that handles them The post Loop Engineering for Listing Questions: When the Answer Is Every Passage, Not the Top One appeared first on Towards Data Science .
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 .
A preprocessing pipeline let my car price model peek at the test set before the exam, and the twelve points of R squared it cheated its way to The post My Model Was Cheating on Its Own Test appeared first on Towards Data Science .