ChainForge: A Visual Toolkit for Prompt Engineering and LLM Hypothesis Testing
This tool addresses the problem of accessible and flexible LLM evaluation for researchers and practitioners, though it is incremental as it builds on existing prompt engineering concepts with a new interface.
The authors tackled the challenge of evaluating large language model outputs by developing ChainForge, an open-source visual toolkit for prompt engineering and hypothesis testing, which enabled users to investigate hypotheses in real-world settings through a graphical interface.
Evaluating outputs of large language models (LLMs) is challenging, requiring making -- and making sense of -- many responses. Yet tools that go beyond basic prompting tend to require knowledge of programming APIs, focus on narrow domains, or are closed-source. We present ChainForge, an open-source visual toolkit for prompt engineering and on-demand hypothesis testing of text generation LLMs. ChainForge provides a graphical interface for comparison of responses across models and prompt variations. Our system was designed to support three tasks: model selection, prompt template design, and hypothesis testing (e.g., auditing). We released ChainForge early in its development and iterated on its design with academics and online users. Through in-lab and interview studies, we find that a range of people could use ChainForge to investigate hypotheses that matter to them, including in real-world settings. We identify three modes of prompt engineering and LLM hypothesis testing: opportunistic exploration, limited evaluation, and iterative refinement.