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 parameters

5. Add streaming support

6. Implement error handling

#### Copy-Paste Prompts

Use @llm-application-dev-ai-assistant to build conversational AI

Use @llm-application-dev-langchain-agent to create LangChain agents

Use @llm-application-dev-prompt-optimize to optimize prompts

Phase 3: RAG Implementation

#### Skills to Invoke

- `rag-engineer` - RAG engineering

- `rag-implementation` - RAG implementation

- `embedding-strategies` - Embedding selection

- `vector-database-engineer` - Vector databases

- `similarity-search-patterns` - Similarity search

- `hybrid-search-implementation` - Hybrid search

#### Actions

1. Design data pipeline

2. Choose embedding model

3. Set up vector database

4. Implement chunking strategy

5. Configure retrieval

6. Add reranking

7. Implement caching

#### Copy-Paste Prompts

Use @rag-engineer to design RAG pipeline

Use @vector-database-engineer to set up vector search

Use @embedding-strategies to select optimal embeddings

Phase 4: AI Agent Development

#### Skills to Invoke

- `autonomous-agents` - Autonomous agent patterns

- `autonomous-agent-patterns` - Agent patterns

- `crewai` - CrewAI framework

- `langgraph` - LangGraph

- `multi-agent-patterns` - Multi-agent systems

- `computer-use-agents` - Computer use agents

#### Actions

1. Design agent architecture

2. Define agent roles

3. Implement tool integration

4. Set up memory systems

5. Configure orchestration

6. Add human-in-the-loop

#### Copy-Paste Prompts

Use @crewai to build role-based multi-agent system

Use @langgraph to create stateful AI workflows

Use @autonomous-agents to design autonomous agent

Phase 5: ML Pipeline Development

#### Skills to Invoke

- `ml-engineer` - ML engineering

- `mlops-engineer` - MLOps

- `machine-learning-ops-ml-pipeline` - ML pipelines

- `ml-pipeline-workflow` - ML workflows

- `data-engineer` - Data engineering

#### Actions

1. Design ML pipeline

2. Set up data processing

3. Implement model training

4. Configure evaluation

5. Set up model registry

6. Deploy models

#### Copy-Paste Prompts

Use @ml-engineer to build machine learning pipeline

Use @mlops-engineer to set up MLOps infrastructure

Phase 6: AI Observability

#### Skills to Invoke

- `langfuse` - Langfuse observability

- `manifest` - Manifest telemetry

- `evaluation` - AI evaluation

- `llm-evaluation` - LLM evaluation

#### Actions

1. Set up tracing

2. Configure logging

3. Implement evaluation

4. Monitor performance

5. Track costs

6. Set up alerts

#### Copy-Paste Prompts