ai-ml

AI/ML Workflow Bundle

Overview

Comprehensive AI/ML workflow for building LLM applications, implementing RAG systems, creating AI agents, and developing machine learning pipelines. This bundle orchestrates skills for production AI development.

When to Use This Workflow

Use this workflow when:

• Building LLM-powered applications
• Implementing RAG (Retrieval-Augmented Generation)
• Creating AI agents
• Developing ML pipelines
• Adding AI features to applications
• Setting up AI observability

Workflow Phases

Phase 1: AI Application Design

Skills to Invoke

• ai-product - AI product development
• ai-engineer - AI engineering
• ai-agents-architect - Agent architecture
• llm-app-patterns - LLM patterns

Actions

1. Define AI use cases
2. Choose appropriate models
3. Design system architecture
4. Plan data flows
5. Define success metrics

Copy-Paste Prompts

Use @ai-product to design AI-powered features

Use @ai-agents-architect to design multi-agent system

Phase 2: LLM Integration

Skills to Invoke

• llm-application-dev-ai-assistant - AI assistant development
• llm-application-dev-langchain-agent - LangChain agents
• llm-application-dev-prompt-optimize - Prompt engineering
• gemini-api-dev - Gemini API

Actions

1. Select LLM provider
2. Set up API access
3. Implement prompt templates
4. Configure model