Building applications utilizing large language models (LLMs) poses challenges for developers, particularly due to the lack of educational resources akin to those available for system design.
A simple 'simple wrapper' architecture centers around the concept of Tools, which are defined as self-contained pieces of code performing specific actions that LLMs can understand.
Tools must provide LLM-understandable documentation and should only accept inputs of data types supported by the respective LLM to ensure compatibility and functionality.
This article aims to equip developers with foundational concepts that will enhance their ability to create scalable and supportable LLM wrappers, recognizing the complexity of the topic.
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