Building a conversation engine in Python that shows how LLMs fake memory (heymeraki.substack.com)

A new lesson on building a conversation engine in Python demonstrates how large language models (LLMs) create the illusion of memory. Instead of truly remembering past interactions, LLMs function by receiving the entire conversation history with each message, allowing them to generate contextually aware responses. The lesson guides users through creating a script that simulates an ongoing dialogue by maintaining a growing list of conversation exchanges, showcasing the mechanics behind how AI chats manage context. This development is significant for the AI/ML community as it highlights a core limitation of current LLMs: while they can provide coherent responses, they do not possess genuine memory or understanding. The tutorial outlines essential technical components, such as integrating the Anthropic API, managing conversation history in Python, and implementing a loop that keeps the dialogue active until the user decides to quit. By demonstrating this foundational concept, the lesson equips developers with the knowledge to understand and build upon existing AI conversational frameworks.