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ai-feature-builder

Guide development of production AI features with LLMs, RAG pipelines, and guardrails.

1|1|Updated Mar 9, 2026
Search Tags:#prompt engineering#feature development#ai#llm#rag#guardrails
One-click install
npx skills add https://github.com/Cure-Consulting-Group/ProductEngineeringSkills --skill ai-feature-builder
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: ai-feature-builder
Source: https://github.com/Cure-Consulting-Group/ProductEngineeringSkills/tree/main/skills/ai-feature-builder
Command: npx skills add https://github.com/Cure-Consulting-Group/ProductEngineeringSkills --skill ai-feature-builder

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill streamlines the development of production-ready AI features, ensuring they are reliable, cost-effective, and safe for end-users.

Core Features & Use Cases

  • AI Feature Development: Guides users through building AI features like chatbots, smart search, and content generation.
  • Architecture Guidance: Provides patterns for direct LLM calls, RAG, and multi-step agentic workflows.
  • Implementation Best Practices: Details prompt engineering, guardrails, cost management, and streaming.
  • Use Case: Develop a RAG-powered customer support chatbot that answers questions based on your company's knowledge base.

Quick Start

Use the ai-feature-builder skill to create a new chatbot feature named 'customer-support-bot'.

Frequently Asked Questions about ai-feature-builder

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I build production AI features with LLMs and RAG pipelines?▼

Building production AI features requires integrating LLMs and RAG pipelines using established architecture patterns. This includes implementing prompt engineering, input/output guardrails, cost management, streaming, and error handling for reliable deployment.

What architecture patterns should I use for multi-step agentic workflows?▼

Architecture patterns for multi-step agentic workflows include direct LLM calls, RAG pipelines, and multi-step agent designs. Choosing the appropriate pattern ensures reliable and safe deployment for complex AI feature development.

How do I implement guardrails for safe LLM deployment?▼

Implementing guardrails for safe LLM deployment requires establishing input and output validation rules. These guardrails filter content and manage interactions to ensure AI features remain reliable and safe for end-users.

What's the best way to manage costs when streaming LLM responses?▼

Managing costs when streaming LLM responses involves applying cost management rules alongside streaming implementation. This approach monitors token usage and handles errors efficiently to keep production AI features cost-effective.

Can I use this approach to develop a RAG-powered customer support chatbot?▼

Yes, you can develop a RAG-powered customer support chatbot using these architecture patterns. The RAG pipeline integrates with your company knowledge base to answer user questions accurately and safely.

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