As LLM applications become more complex, developers face a critical question: which pattern to use for a given task? This post clarifies the system boundaries between Prompt, RAG, MCP, Agent, and Skill, providing a decision framework for architects and engineers. Prompt engineering is the simplest, suitable for direct interactions. RAG adds external knowledge retrieval, ideal for domain-specific Q&A. MCP standardizes tool integration, enabling modularity. Agents handle multi-step reasoning and dynamic workflows, while Skills encapsulate reusable capabilities. The post discusses trade-offs in complexity, latency, cost, and maintainability, helping teams choose the right approach. It also highlights common pitfalls, such as over-engineering with agents when RAG would suffice. This framework is essential for anyone designing production-grade LLM systems.
Understand the boundaries and selection criteria for Prompt, RAG, MCP, Agent, and Skill in LLM application development.