Design, validate, test, and ship scalable prompt-driven features with safety checks.
npx skills add https://github.com/caobingsheng/skills --skill ai-product-caobingsheng
Please help me install this Agent Skill. Skill: ai-product Source: https://github.com/caobingsheng/skills/tree/main/product/ai-product Command: npx skills add https://github.com/caobingsheng/skills --skill ai-product-caobingsheng
You are an AI product engineer who has shipped LLM features to millions of users. You've debugged hallucinations at 3am, optimized prompts to reduce costs by 80%, and built safety systems that caught thousands of harmful outputs. You know that demos are easy and production is hard.
Outline a step-by-step plan to design, validate, test, and ship a scalable prompt-driven feature with safety checks.
High-intent search queries and answers about installing and using this skill.
Streaming LLM responses reduces perceived latency by showing progress to users as tokens generate. This approach is critical for scalable AI features where long generation times would otherwise cause user frustration and application timeouts.
Versioning and testing prompts involves storing prompt versions in code and running regression suites against them. This catches performance degradations and format breaks before deployment, ensuring reliable AI feature updates at scale.
Managing LLM API costs requires proactive token budgeting and cost awareness monitoring across web applications. This prevents unexpected billing spikes while maintaining feature quality and throughput for millions of users.
Safety guardrails for reliable AI features require defense-in-depth strategies including input validation, output filtering, and risk management systems. These proactively catch harmful outputs and mitigate sharp edges before they reach users.
Demos fail in production due to unstructured output parsing, context window stuffing, and lack of validation. Shipping reliable AI features requires prompt testing, structured outputs, and safety guardrails to handle real-world edge cases.
DavidLam-ossRoute product requirements into L0, L1, or L2 development workflows.
floflo777Evaluate RAG systems using retrieval, generation, and latency metrics.
mymx2Configure GitHub Actions workflows to enforce quality gates on pull requests.
RefoundAIGuide non-technical teams to build runnable prototypes with AI coding tools.
