ai-engineer

SKILL.md

Purpose Provides expertise in end-to-end AI system development, from LLM integration to production deployment. Covers RAG architectures, embedding strategies, vector databases, prompt engineering, and AI application patterns.

| Prompt in code | Hard to iterate and test | Use prompt templates with versioning | | No evaluation | Unknown quality in production | Implement eval pipelines | | Synchronous LLM calls | Slow user experience | Use streaming responses | | Unbounded context | Token limits and cost | Implement context windowing |

| No fallbacks | System fails on API errors | Add retry logic and alternatives |

Expert in building comprehensive AI systems, integrating LLMs, RAG architectures, and autonomous agents into production applications. Use when building AI-powered features, implementing LLM integrations, designing RAG pipelines, or deploying AI systems. Source: 404kidwiz/claude-supercode-skills.

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