# Low-code LLM: Graphical User Interface over Large Language Models

arXiv: https://arxiv.org/abs/2304.08103 · HTML: https://mlsanity.com/paper/2304.08103/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 31 references that are in the map.
- First posted on arXiv 17 Apr 2023.

## Builds on (most cited first, 13 of 13)

- [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021)
- [Sparks of Artificial General Intelligence: Early experiments with GPT-4](https://mlsanity.com/paper/2303.12712/) (2023)
- [Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?](https://mlsanity.com/paper/2202.12837/) (2022)
- [Large Language Models Are Human-Level Prompt Engineers](https://mlsanity.com/paper/2211.01910/) (2022)
- [A Multitask, Multilingual, Multimodal Evaluation of ChatGPT on Reasoning, Hallucination, and Interactivity](https://mlsanity.com/paper/2302.04023/) (2023)
- [Visual ChatGPT: Talking, Drawing and Editing with Visual Foundation Models](https://mlsanity.com/paper/2303.04671/) (2023)
- [Capabilities of GPT-4 on Medical Challenge Problems](https://mlsanity.com/paper/2303.13375/) (2023)
- [A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT](https://mlsanity.com/paper/2302.11382/) (2023)
- [Language Models can Solve Computer Tasks](https://mlsanity.com/paper/2303.17491/) (2023)
- [RLPrompt: Optimizing Discrete Text Prompts with Reinforcement Learning](https://mlsanity.com/paper/2205.12548/) (2022)
- [TaskMatrix.AI: Completing Tasks by Connecting Foundation Models with Millions of APIs](https://mlsanity.com/paper/2303.16434/) (2023)
- [A Categorical Archive of ChatGPT Failures](https://mlsanity.com/paper/2302.03494/) (2023)
- [PromptChainer: Chaining Large Language Model Prompts through Visual Programming](https://mlsanity.com/paper/2203.06566/) (2022)

## Built on by (most cited first, 13 of 13)

- [MLLM-as-a-Judge: Assessing Multimodal LLM-as-a-Judge with Vision-Language Benchmark](https://mlsanity.com/paper/2402.04788/) (2024)
- [WaitGPT: Monitoring and Steering Conversational LLM Agent in Data Analysis with On-the-Fly Code Visualization](https://mlsanity.com/paper/2408.01703/) (2024)
- [SPHERE: An Evaluation Card for Human-AI Systems](https://mlsanity.com/paper/2504.07971/) (2025)
- [ScamAgents: How AI Agents Can Simulate Human-Level Scam Calls](https://mlsanity.com/paper/2508.06457/) (2025)
- [Generating a Low-code Complete Workflow via Task Decomposition and RAG](https://mlsanity.com/paper/2412.00239/) (2024)
- [Composable Prompting Workspaces for Creative Writing: Exploration and Iteration Using Dynamic Widgets](https://mlsanity.com/paper/2503.21394/) (2025)
- [StarFlow: Generating Structured Workflow Outputs From Sketch Images](https://mlsanity.com/paper/2503.21889/) (2025)
- [iToT: An Interactive System for Customized Tree-of-Thought Generation](https://mlsanity.com/paper/2409.00413/) (2024)
- [AIPOM: Agent-aware Interactive Planning for Multi-Agent Systems](https://mlsanity.com/paper/2509.24826/) (2025)
- [Componentization: Decomposing Monolithic LLM Responses into Manipulable Semantic Units](https://mlsanity.com/paper/2509.08203/) (2025)
- [Qualitative Evaluation of LLM-Designed GUI](https://mlsanity.com/paper/2601.22759/) (2026)
- [Lazy or Efficient? Towards Accessible Eye-Tracking Event Detection Using LLMs](https://mlsanity.com/paper/2604.13243/) (2026)
- [Gricea: An Open Science Platform for Conversational AI Research](https://mlsanity.com/paper/2609.22039/) (2026)

## Abstract (arXiv)

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.