Code Is All You Need: The Limitations of Multi-Component Pipelines (MCPs)

This article challenges the practicality of Multi-Component Pipelines (MCPs) for many tasks, arguing that their heavy reliance on inference makes them inefficient and difficult to scale. The author uses a personal example – converting reStructuredText to Markdown – to demonstrate a superior approach: using LLMs to generate code that performs the task, followed by LLM-based validation. This method reduces inference dependency, enhances reliability, and scales well, especially for repetitive tasks. While acknowledging MCP's strengths in niche scenarios, the author concludes that its inherent limitations hinder large-scale automation. The future, they suggest, lies in developing more effective code generation techniques coupled with LLM validation and explanation to improve usability and applicability.
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