Large language models (LLMs) are the result of a massive experiment in bottom-up, data-driven reverse engineering of language at scale. Despite their utility in a number of downstream NLP tasks, ample research has shown that LLMs are incapable of performing reasoning in tasks that require quantification over and the manipulation of symbolic variables (e.g., planning and general problem solving) - see for example [25][26]. In this document, however, we will focus on testing LLMs for their language understanding capabilities, their supposed forte. In this regard we believe that we have not been testing the language understanding capabilities of large language models (LLMs) properly. Prompting LLMs and asking for responses will always look impressive because that's how LLMs were designed, i.e., to generate text. The proper method of testing the understanding capabilities of LLMs, we argue, is to prompt LLMs in reverse: give the LLM a snippet of text and query their understanding of the input text by asking the LLM questions against the input text. As we will show here the language understanding capabilities of LLMs have been widely exaggerated. By testing the understanding capabilities properly - i.e., by giving the LLM snippets of text as input and then querying what the LLM "understood" it will become apparent that LLMs do not truly understand language, beyond very superficial inferences that are essentially the byproduct of the memorization of massive amounts of ingested text.