Limitations And Assumptions
The Limitations and Assumptions (LA) approach is a methodology for enhancing the quality and reliability of AI-generated notes. When applied alongside the QEC (Question, Evidence, Conclusion) framework, LA helps users critically evaluate the boundaries and foundational premises of AI-generated content. This dual-framework approach addresses a common challenge with AI systems: distinguishing between claims supported by solid evidence and those that rely on unstated assumptions or operate within constrained scopes.
Core Function
The LA method works by explicitly documenting two categories of information. Limitations identify the boundaries of what an AI system knows or can reliably address—such as knowledge cutoff dates, gaps in training data, or topics outside its competency. Assumptions surface the foundational premises embedded in AI responses, including unstated preconditions, value judgments, or contextual factors that shape the output. By making these elements visible, the approach reduces the risk of treating incomplete or conditionally-true information as universal fact.
Integration with QEC
When combined with the QEC framework, which structures thinking around questions, supporting evidence, and conclusions, the LA approach adds a critical validation layer. While QEC helps organize and evaluate the logical structure of claims, LA ensures users recognize what is not being claimed, what data gaps exist, and what unstated conditions affect the conclusion’s applicability. This complementary relationship makes both frameworks more effective at producing notes that are accurate, honest about their boundaries, and appropriately qualified.