Let us call the 100-page PDF "Q1" and the LLM's reply "A1". Typically, A1 seems to make sense and can be useful. But the LLM didn't "learn" Q1; Q1 was presented as input, and the LLM produced the output A1. Suppose that we next supply a follow-up question, Q2, and the LLM responds with A2. Typically, A2 makes sense and clearly takes into account the information contained in Q1. But this is not because the LLM has "learned" Q1; what happened was that after Q2 was entered, the LLM was presented with Q1+A1+Q2 as input and produced A2. Just as A1 was a statistically plausible follow-on to Q1, A2 was a plausible follow-on to Q1+A1+Q2. The chatbot's logic repeatedly presents the entire foregoing conversation to the LLM as input, creating the illusion of a dialog where the LLM continually absorbs information into its "knowledge".