  Utilizing Large Language Models (LLMs) for complex tasks is challenging,
often involving a time-consuming and uncontrollable prompt engineering process.
This paper introduces a novel human-LLM interaction framework, Low-code LLM. It
incorporates six types of simple low-code visual programming interactions to
achieve more controllable and stable responses. Through visual interaction with
a graphical user interface, users can incorporate their ideas into the process
without writing trivial prompts. The proposed Low-code LLM framework consists
of a Planning LLM that designs a structured planning workflow for complex
tasks, which can be correspondingly edited and confirmed by users through
low-code visual programming operations, and an Executing LLM that generates
responses following the user-confirmed workflow. We highlight three advantages
of the low-code LLM: user-friendly interaction, controllable generation, and
wide applicability. We demonstrate its benefits using four typical
applications. By introducing this framework, we aim to bridge the gap between
humans and LLMs, enabling more effective and efficient utilization of LLMs for
complex tasks. The code, prompts, and experimental details are available at
https://github.com/moymix/TaskMatrix/tree/main/LowCodeLLM. A system
demonstration video can be found at
https://www.youtube.com/watch?v=jb2C1vaeO3E.
