From Personal Prompts to a Team Production Line: A 0‑to‑1 Guide for Skill Engineering

This article presents a step‑by‑step method for turning scattered personal prompt knowledge into versioned, testable, and shareable AI workflow assets, covering directory conventions, description design, test case creation, Bad Case feedback loops, Git management, team sharing mechanisms, and a 30‑day rollout plan.

AI workflowGitPrompt engineering

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From Personal Prompts to a Team Production Line: A 0‑to‑1 Guide for Skill Engineering

Wu Shixiong's Large Model Academy

Dec 12, 2025 · Artificial Intelligence

Why Fixing Bad Cases Beats Adding More Data in RLHF

In industrial RLHF, repairing bad cases—structural error samples—provides explicit alignment signals that improve model capability far more efficiently than simply increasing data volume, because it teaches the model how to correct mistakes rather than just exposing it to more examples.

Capability ImprovementModel AlignmentRLHF

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Why Fixing Bad Cases Beats Adding More Data in RLHF

DataFunTalk

Nov 26, 2021 · Artificial Intelligence

Solving Model Prediction Errors: A Comprehensive Bad‑Case Treatment Methodology

This article presents a step‑by‑step methodology for diagnosing and fixing model prediction errors—especially bad cases—in NLP and search systems, covering sample bias, threshold selection, preprocessing, post‑processing, validation cycles, and guidance on when to replace the model.

NLPPostProcessingPreprocessing

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Solving Model Prediction Errors: A Comprehensive Bad‑Case Treatment Methodology