Limitations And Assumptions
The Limitations and Assumptions (LA) approach is a methodology designed to enhance the quality and reliability of AI-generated notes. It functions by explicitly identifying the boundaries of AI-generated content and the foundational premises upon which those outputs are built. This process assists users in critically evaluating the scope of the information provided, ensuring that the context and constraints of the AI’s knowledge base are clearly understood.
When applied alongside the QEC (Question, Evidence, Conclusion) framework, the LA approach creates a dual-layered verification system. The QEC framework structures the logical flow of the note by defining the inquiry, supporting data, and resulting inference. The LA component complements this by annotating the reliability of each layer, highlighting where data may be sparse, speculative, or derived from specific temporal or contextual constraints.
This integration allows for more robust knowledge management by separating factual assertions from their underlying uncertainties. Users can distinguish between core conclusions supported by strong evidence and those reliant on broader assumptions. Consequently, the combined method reduces the risk of misinterpretation and supports more accurate decision-making based on AI-assisted documentation.