Chatbots based on large language models (LLMs) such as ChatGPT, Claude, and Grok have become increasingly complex and sophisticated over the last few years. The conversation-like text generated by these systems is of sufficient quality that both humans and software-based detection techniques frequently encounter difficulty distinguishing between LLMs and humans based on the text of a conversation alone. No matter how complex the LLM, however, it is ultimately a mathematical model of its training data, and it lacks the human ability to determine whether or not a conversation in which it participates truly has meaning, or is simply a sequence of gibberish responses.A consequence of this state of affairs is that an LLM will continue to engage in a “conversation” comprised of nonsense long past the point where a human would have abandoned the discussion as pointless. This can be demonstrated experimentally by having an LLM-based chatbot “converse” with simpler text generation bots that would be inadequate to fool humans. This article examines how a chatbot based on an open-source LLM (Llama 3.1, 8B version) reacts to attempts to get it to engage in endless exchanges with the following four basic text generation bots:a bot that simply asks the LLM the same question over and overa bot that sends the LLM random fragments of a work of fictiona bot that asks the LLM randomly generated questionsa bot that repeatedly asks the LLM what it meant by its most recent responseimport json import ollama import random import sys import time LLM_MODEL…Baiting the bot