ai-engineer - Build production LLM applications, RAG, and AI agents.
Implements LLM applications, RAG systems, vector search, agent orchestration, multimodal AI, and enterprise AI integrations.
Tags
Updated: 2026-09-19Capabilities
Typical Inputs
Typical Outputs
What this skill does
- Integrate LLM models and serving frameworks
- Build multi-stage retrieval RAG pipelines
- Orchestrate multi-agent workflows and memory
- Optimize vector indexing and search
- Implement prompt engineering and safety controls
- Integrate multimodal vision and audio models
- Build AI data preprocessing pipelines
Inputs
- User prompts and queries
- Documents and multimedia files
- Enterprise system data and APIs
- Vector database connection parameters
Outputs
- Generated responses and streaming inference
- Retrieved context and ranked search results
- Structured outputs and model predictions
- AI service APIs and webhooks
Requirements
- Access to LLM APIs or local inference engines
- Vector database infrastructure
- Python runtime environment with AI libraries