mlsanityData to 7 Oct 2026

PromptChainer: Chaining Large Language Model Prompts through Visual Programming

arXiv 2203.06566 ↗First posted 13 Mar 2022cs.HCcited by 134

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Abstract (arXiv)

While LLMs can effectively help prototype single ML functionalities, many real-world applications involve complex tasks that cannot be easily handled via a single run of an LLM. Recent work has found that chaining multiple LLM runs together (with the output of one step being the input to the next) can help users accomplish these more complex tasks, and in a way that is perceived to be more transparent and controllable. However, it remains unknown what users need when authoring their own LLM chains -- a key step for lowering the barriers for non-AI-experts to prototype AI-infused applications. In this work, we explore the LLM chain authoring process. We conclude from pilot studies find that chaining requires careful scaffolding for transforming intermediate node outputs, as well as debugging the chain at multiple granularities; to help with these needs, we designed PromptChainer, an interactive interface for visually programming chains. Through case studies with four people, we show that PromptChainer supports building prototypes for a range of applications, and conclude with open questions on scaling chains to complex tasks, and supporting low-fi chain prototyping.

Key facts. Cited by 134 papers in the map (data through 7 Oct 2026). First posted on arXiv 13 Mar 2022.

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Citing papers per year: 2022: 8; 2023: 50; 2024: 36; 2025: 30; 2026: 10.

134 papers.

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  1. 2023Generative Agents: Interactive Simulacra of Human Behaviorcited by 3,500
  2. 2022Emergent Abilities of Large Language Modelscited by 2,483
  3. 2023Graph of Thoughts: Solving Elaborate Problems with Large Language Modelscited by 873
  4. 2024Large Language Models: A Surveycited by 506
  5. 2023Cognitive Architectures for Language Agentscited by 422
  6. 2023Augmented Language Models: a Surveycited by 398
  7. 2023Character-LLM: A Trainable Agent for Role-Playingcited by 335
  8. 2022Reasoning with Language Model Prompting: A Surveycited by 251
  9. 2024Who Validates the Validators? Aligning LLM-Assisted Evaluation of LLM Outputs with Human Preferencescited by 142
  10. 2022Prompting Is Programming: A Query Language for Large Language Modelscited by 110
  11. 2022Language Model Cascadescited by 104
  12. 2023ChainForge: A Visual Toolkit for Prompt Engineering and LLM Hypothesis Testingcited by 88
38 more
  1. 2025Interactive Debugging and Steering of Multi-Agent AI Systemscited by 68
  2. 2023MathChat: Converse to Tackle Challenging Math Problems with LLM Agentscited by 67
  3. 2023Graphologue: Exploring Large Language Model Responses with Interactive Diagramscited by 65
  4. 2024StateFlow: Enhancing LLM Task-Solving through State-Driven Workflowscited by 65
  5. 2024Demystifying Chains, Trees, and Graphs of Thoughtscited by 63
  6. 2023PromptMagician: Interactive Prompt Engineering for Text-to-Image Creationcited by 58
  7. 2023Let's Think Outside the Box: Exploring Leap-of-Thought in Large Language Models with Creative Humor Generationcited by 56
  8. 20223DALL-E: Integrating Text-to-Image AI in 3D Design Workflowscited by 41
  9. 2023Boosting Logical Reasoning in Large Language Models through a New Framework: The Graph of Thoughtcited by 41
  10. 2025Vibe Coding vs. Agentic Coding: Fundamentals and Practical Implications of Agentic AIcited by 39
  11. 2023Natural Language Processing in the Legal Domaincited by 38
  12. 2023Choice Over Control: How Users Write with Large Language Models using Diegetic and Non-Diegetic Promptingcited by 36
  13. 2023CoQuest: Exploring Research Question Co-Creation with an LLM-based Agentcited by 34
  14. 2023ConstitutionMaker: Interactively Critiquing Large Language Models by Converting Feedback into Principlescited by 33
  15. 2023Foundation Models Meet Visualizations: Challenges and Opportunitiescited by 31
  16. 2024How Far Are We From AGI: Are LLMs All We Need?cited by 31
  17. 2023LLMs as Workers in Human-Computational Algorithms? Replicating Crowdsourcing Pipelines with LLMscited by 27
  18. 2023RELIC: Investigating Large Language Model Responses using Self-Consistencycited by 23
  19. 2023ScatterShot: Interactive In-context Example Curation for Text Transformationcited by 20
  20. 2024Intelligent Clinical Documentation: Harnessing Generative AI for Patient-Centric Clinical Note Generationcited by 20
  21. 2023ReelFramer: Human-AI Co-Creation for News-to-Video Translationcited by 19
  22. 2024SPADE: Synthesizing Data Quality Assertions for Large Language Model Pipelinescited by 19
  23. 2024Prompts Are Programs Too! Understanding How Developers Build Software Containing Promptscited by 19
  24. 2022LegalBench: Prototyping a Collaborative Benchmark for Legal Reasoningcited by 17
  25. 2023ABScribe: Rapid Exploration & Organization of Multiple Writing Variations in Human-AI Co-Writing Tasks using Large Language Modelscited by 17
  26. 2023Designing LLM Chains by Adapting Techniques from Crowdsourcing Workflowscited by 17
  27. 2022In-BoXBART: Get Instructions into Biomedical Multi-Task Learningcited by 16
  28. 2023Iterated Decomposition: Improving Science Q&A by Supervising Reasoning Processescited by 15
  29. 2023"It Felt Like Having a Second Mind": Investigating Human-AI Co-creativity in Prewriting with Large Language Modelscited by 15
  30. 2023VAL: Interactive Task Learning with GPT Dialog Parsingcited by 14
  31. 2024Mitigating Backdoor Threats to Large Language Models: Advancement and Challengescited by 14
  32. 2025ProactiveVA: Proactive Visual Analytics with LLM-Based UI Agentcited by 14
  33. 2022BioTABQA: Instruction Learning for Biomedical Table Question Answeringcited by 13
  34. 2023'Generative CI' through Collective Response Systemscited by 13
  35. 2023Low-code LLM: Graphical User Interface over Large Language Modelscited by 13
  36. 2023ANPL: Towards Natural Programming with Interactive Decompositioncited by 13
  37. 2023Next Steps for Human-Centered Generative AI: A Technical Perspectivecited by 13
  38. 2024Not Just Novelty: A Longitudinal Study on Utility and Customization of an AI Workflowcited by 13

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