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 specific constraints.
Core Function
The LA approach prompts users to identify and document two key aspects of AI-generated content. Limitations refer to the boundaries of what the AI can reliably address—such as knowledge cutoffs, domain expertise gaps, or contextual constraints. Assumptions are the underlying premises on which the AI’s responses are built, which may include particular interpretations of ambiguous terms, unstated background knowledge, or methodological choices. By explicitly naming these elements, users gain transparency into how conclusions were reached.
Application with QEC Framework
When combined with the QEC framework, LA complements the structure of analyzing questions, evaluating evidence, and forming conclusions by adding a critical layer of scrutiny. While QEC helps organize logical reasoning, LA prevents users from accepting AI outputs uncritically by requiring them to recognize what the system cannot know or has presumed. This pairing is particularly valuable for high-stakes applications where understanding the confidence boundaries of generated content directly affects decision-making quality.