domains / families / software-engineering / ai-engineer

ai-engineer

combined

Build production-ready LLM applications, advanced RAG systems, and Build production-ready LLM applications, advanced RAG systems, and intelligent agents. Implements vector search, multimodal AI, agent orchestration, and enterprise AI integrations.

When to use

• Building or improving LLM features, RAG systems, or AI agents
• Designing production AI architectures and model integration
• Optimizing vector search, embeddings, or retrieval pipelines
• Implementing AI safety, monitoring, or cost controls
• Expert AI engineer specializing in LLM application development, RAG systems, and AI agent architectures. Masters both traditional and cutting-edge generative AI patterns, with deep knowledge of the modern AI stack including vector database…

When not to use

• The task is pure data science or traditional ML without LLMs
• You only need a quick UI change unrelated to AI features
• There is no access to data sources or deployment targets
• A live deployment is requested but the required target access is unavailable; architecture, local fixtures and review can still proceed
• 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.

Skill page

Conflict

The two copies share the same core procedure (clarify, design, implement, validate) and most capability sections, but they disagree on how to handle model selection and prompt engineering. The first copy lists specific model names as capabilities, while the second copy explicitly warns that those names are illustrative and requires checking current availability, and it replaces chain-of-thought prompting with observable verification. Crowning one copy would hide the other's safety and currency constraints.