AI LLM Application

🤖 AI / LLM Application

Group Skill — Orchestrates sub-skills for building an application powered by LLMs. Activate by saying: "Build an AI app" or "LLM application"

When to Use

• Building an AI-powered application
• Need to integrate with LLM providers
• Implementing RAG, agents, or prompt engineering

Step-by-Step Workflow

Step 1: Provider

Skill: gemini-api-dev / azure-ai-projects-py / azure-ai-projects-ts

• Choose LLM provider
• Set up API access
• Configure authentication

Step 2: Patterns

Skill: llm-app-patterns / llm-structured-output

• Define app architecture
• Design prompt flows
• Handle structured outputs

Step 3: RAG

Skill: rag-engineer / rag-implementation

• Implement retrieval-augmented generation
• Set up vector databases
• Build embedding pipelines

Step 4: Agents

Skill: langchain-architecture / langgraph / crewai

• Design agent orchestration
• Implement tool use
• Handle multi-agent coordination

Step 5: Prompts

Skill: prompt-engineering / prompt-engineering-patterns

• Optimize prompts
• Use structured prompt patterns
• Manage prompt templates

Step 6: Evaluation

Skill: llm-evaluation / advanced-evaluation

• Set up quality assurance
• Evaluate LLM outputs
• Track performance metrics

Step 7: Safety

Skill: yes-md / tool-use-guardian

• Implement AI governance
• Add content safety filters
• Monitor for misuse

Step 8: Monitoring

Skill: langfuse / posthog-automation

• Track LLM calls and costs
• Monitor latency and errors
• Analyze user interactions

Completion

AI application is built with RAG, agents, safety, and monitoring. 🤖

Sub-Skills Referenced

1. gemini-api-dev
2. azure-ai-projects-py
3. azure-ai-projects-ts
4. llm-app-patterns
5. llm-structured-output
6. rag-engineer
7. rag-implementation
8. langchain-architecture
9. langgraph
10. crewai
11. prompt-engineering
12. prompt-engineering-patterns
13. llm-evaluation
14. advanced-evaluation
15. yes-md
16. tool-use-guardian
17. langfuse
18. posthog-automation