PromptChainer: Chaining Large Language Model Prompts through Visual Programming - YouTube

PromptChainer: Chaining Large Language Model Prompts through Visual ProgrammingTongshuang Wu, Ellen Jiang, Aaron Donsbach, Jeff Gray, Alejandra Molina, Micha...

PromptChainer: Chaining Large Language Model Prompts through Visual Programming Tongshuang Wu, Ellen Jiang, Aaron Donsbach, Jeff Gray, Alejandra Molina, Michael Terry, Carrie J Cai CHI'22: ACM Conference on Human Factors in Computing Systems Session: Late Breaking Work (LBW) Abstract 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…

saved by

• Paul Shen

related reading

• [2201.11903] Chain of Thought Prompting Elicits Reasoning in Large Language Modelsarxiv.org
• Circuit Tracing: Revealing Computational Graphs in Language Modelstransformer-circuits.pub
• LLM Powered Autonomous Agents | Lil'Loglilianweng.github.io
• Prompt Chaining | Prompt Engineering Guidepromptingguide.ai
• 2402.07927arxiv.org
• A Mechanistic Explanation of Prompt Injection (and why you should study roles) — LessWronglesswrong.com
• [2201.11903] Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelsarxiv.org
• Re-implementing LangChain in 100 lines of codeblog.scottlogic.com
• [2508.19227] Generative Interfaces for Language Modelsarxiv.org
• [2309.16797] Promptbreeder: Self-Referential Self-Improvement via Prompt Evolutionar5iv.labs.arxiv.org
• The Problem With LangChain | Max Woolf's Blogminimaxir.com
• Natural Language Is an Unnatural Interfacevarunshenoy.substack.com