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AI Feature Implementation

Automate AI feature development with LLM integration, RAG pipelines, and guardrails.

1|Updated Mar 11, 2026
Search Tags:#testing#architecture#ai#llm#rag#prompts#guardrails
One-click install
npx skills add https://github.com/BasharAmso/Bashi --skill ai-feature-implementation
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: AI Feature Implementation
Source: https://github.com/BasharAmso/Bashi/tree/main/create-bashi-app/template/.claude/skills/ai-feature
Command: npx skills add https://github.com/BasharAmso/Bashi --skill ai-feature-implementation

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Automating AI feature development requires integrating LLM calls, RAG pipelines, and guardrails with robust testing and governance.

Core Features & Use Cases

  • LLM integration scaffolding for feature development
  • RAG pipelines for document grounding and retrieval
  • Guardrails and test harnesses with STATE/DECISIONS logging
  • End-to-end lifecycle from PRD to STATE updates

Quick Start

Describe your desired AI feature and run the workflow to generate a production-ready AI service layer.

Frequently Asked Questions about AI Feature Implementation

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

FAQPage Schema
How do I add LLM integration and RAG pipelines to my application with guardrails?▼

To add LLM integration and RAG pipelines with guardrails, you can use an automated workflow that generates a production-ready AI service layer. This provides structured development, input/output guardrails, and token usage logging.

What is the best way to build AI features with input output guardrails and token usage logging?▼

The best way to build AI features with input output guardrails and token usage logging is to use an automated end-to-end development workflow. This scaffolds the LLM integration and enforces governance through STATE/DECISIONS documentation.

How do I set up a RAG pipeline and prompt engineering workflow for production environments?▼

You can set up a RAG pipeline and prompt engineering workflow by running an automated process that generates a production-ready service layer. It satisfies requirements like pinned model versions and dedicated prompts across multiple environments.

Can I use an automated workflow for LLM integration across multiple modules and environments?▼

Yes, you can automate LLM integration across multiple modules and environments. The workflow applies structured guardrails and test harnesses to ensure robust AI feature development from PRD to STATE updates.

Why do I need STATE and DECISIONS documentation when developing AI features?▼

You need STATE and DECISIONS documentation to maintain governance and track the lifecycle of your AI features. It logs automated decisions and ensures robust tracking throughout the end-to-end development process.

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