--- name: ai-ml description: "AI and machine learning workflow covering LLM application development, RAG implementation, agent architecture, ML pipelines, and AI-powered features." category: workflow-bundle risk: safe source: personal date_added: "2026-02-27" --- # 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 ``` Use @langfuse to set up LLM observability ``` ``` Use @evaluation to create evaluation framework ``` ### Phase 7: AI Security #### Skills to Invoke - `prompt-engineering` - Prompt security - `security-scanning-security-sast` - Security scanning #### Actions 1. Implement input validation 2. Add output filtering 3. Configure rate limiting 4. Set up access controls 5. Monitor for abuse 6. Implement audit logging ## AI Development Checklist ### LLM Integration - [ ] API keys secured - [ ] Rate limiting configured - [ ] Error handling implemented - [ ] Streaming enabled - [ ] Token usage tracked ### RAG System - [ ] Data pipeline working - [ ] Embeddings generated - [ ] Vector search optimized - [ ] Retrieval accuracy tested - [ ] Caching implemented ### AI Agents - [ ] Agent roles defined - [ ] Tools integrated - [ ] Memory working - [ ] Orchestration tested - [ ] Error handling robust ### Observability - [ ] Tracing enabled - [ ] Metrics collected - [ ] Evaluation running - [ ] Alerts configured - [ ] Dashboards created ## Quality Gates - [ ] All AI features tested - [ ] Performance benchmarks met - [ ] Security measures in place - [ ] Observability configured - [ ] Documentation complete ## Related Workflow Bundles - `development` - Application development - `database` - Data management - `cloud-devops` - Infrastructure - `testing-qa` - AI testing ## Limitations - Use this skill only when the task clearly matches the scope described above. - Do not treat the output as a substitute for environment-specific validation, testing, or expert review. - Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.