AI & machine learning course

--- 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 test