RAG vs. Skill vs. MCP vs. RLM (blog.alexewerlof.com)

In a comprehensive examination of techniques designed to enhance the reliability of large language models (LLMs) while addressing context window limitations, several strategies are discussed: Retrieval-Augmented Generation (RAG), SKILL, MCP, and RLM. RAG allows LLMs to input up-to-date information from external databases right before processing user prompts, effectively expanding their context length. It is praised for its simplicity and separation from model architecture, although it does rely heavily on data quality and introduces added infrastructure complexity. Alternatively, SKILL empowers LLMs with selective tool access, letting them choose which external capabilities to invoke based on the conversation context, reducing resource consumption and improving response precision. MCP serves as a standardized API gateway, enabling seamless integration with various software systems while maintaining security and reusability. It establishes a clear client-server architecture for LLM interactions, though its complexity can hinder performance in simpler tasks. Lastly, RLM shifts the paradigm by treating lengthy prompts as external variables, effectively bypassing context constraints altogether, albeit requiring a secure sandbox for execution. Each approach carries its own pros and cons, making them suitable for different scenarios within the AI/ML community, from simple Q&A systems to complex, stateful integration tasks.