Treat the Context Window as a Data Assembly Problem (klr-pattern.github.io)

A new approach to context assembly for AI applications, particularly using large language models (LLMs), has emerged, emphasizing the need to treat the context window as a data assembly problem. Traditional AI code, as demonstrated by a typical support context-building function, often leads to scattered data-fetching logic and intertwines data retrieval with the prompt assembly process, making it challenging to optimize, reuse, or scale. The introduction of `pydantic-resolve` aims to address these issues by bringing structure to context assembly, paralleling existing API response assembly techniques. This methodology offers significant benefits for the AI/ML community by highlighting that context assembly should be modeled as a tree structure, similar to API responses. Utilizing tools like `Loader` for data loading and `Collector` for aggregation, this perspective allows for efficient retrieval of related data, reduced N+1 call problems, and clear separation between data fetching and prompt formatting. By applying these established patterns to LLM context assembly, developers can create more maintainable and flexible codebases that can easily adapt to varying data requirements while maximizing performance and minimizing costs.