Chain Forge – evaluate and compare LLMs
Posted: Sun Feb 02, 2025 4:01 am
ChainForge is an open visual programming environment that allows you to easily test and evaluate large language models without having to code. You can query different models and prompts simultaneously to find the best settings. The platform supports you with automated evaluations and clear presentations of the results. ChainForge was developed by Ian Arawjo at Harvard University and is available as a web version at chainforge.ai/play, although this version has limited features. In short, ChainForge makes it easier to experiment with language models and helps you make informed decisions.
Simultaneous queries : Quickly test prompt variations and ideas by querying multiple LLMs simultaneously. This way, you can find the optimal configuration in no time.
Compare answer quality : ChainForge allows you to compare the quality of answers across different prompt permutations, models, and model settings, so you can get the best possible performance from your LLMs.
Automated evaluation metrics : Set up evaluation metrics with code or LLM-based scorers and plot the results automatically. This way you always have an overview of the performance of your models.
Multiple conversations in parallel : Conduct multiple conversations simultaneously using template parameters and chat models. This saves time and enables efficient work.
Templates and Expense Inspection : Create templates for chat messages and inspect or rate the outputs every time. This gives you full control over the conversation flow.
ChainForge goes beyond anecdotal evidence and enables robust evaluation of prompts and models with minimal effort. Filtering and grouping options help you analyze the responses, including formatted tables and exportable data.
Installation and use: How to get started with ChainForge
Installing ChainForge is very easy. You can install it locally using pip:
pip install chainforge chain forge serve
Then open localhost:8000 in a supported browser like Chrome, Firefox, Edge or Brave. Note that you will need to reset your API keys each time as ChainForge does not store them.
Alternatively, a web version with slightly limited macedonia number dataset functions is available at chainforge.ai/play. Here you will also find a handy "Share" button that you can use to generate unique web links for your LLM experiments and share them with others.
Use cases: This is where ChainForge shines in practice
The true strength of ChainForge lies in its versatile application possibilities. Three main areas stand out in particular:
1. Model selection: Find the best LLM for your needs
Choosing the right Language Model is critical to the success of your project. With ChainForge, you can easily compare the performance of different LLMs and identify the best model for your specific needs.
Imagine you want to develop a chatbot system. By comparing different LLMs in ChainForge, you can quickly find out which model provides the most natural and contextual answers. This saves you valuable time and ensures that your chatbot shines with the best possible AI support right from the start.
2. Prompt template design: Optimize your prompts
The quality of your prompts has a huge impact on the output of the LLMs. ChainForge allows you to iteratively improve your prompts and optimize them for the desired results.
Let's say you're working on a project to automatically summarize text. With ChainForge, you can test different prompt variations and evaluate them based on the summaries generated. By making gradual adjustments, you can find the optimal wording to produce precise and meaningful summaries.
3. Hypothesis testing: Understand the capabilities and limitations of LLMs
To use LLMs effectively, it is important to understand their capabilities and limitations. ChainForge allows you to test hypotheses about model behavior and gain valuable insights.
Let's say you want to find out how well an LLM can handle ambiguous or incomplete information. With ChainForge, you can create targeted test cases and analyze the model's responses. This will give you a deeper understanding of the LLM's strengths and weaknesses and enable you to use it optimally for your application.
ChainForge in comparison: What are the differences to other tools?
At first glance, ChainForge may resemble tools like Langflow and Flowise, but the focus is different. While the latter aim at developing complete applications, ChainForge focuses on evaluating and inspecting LLM outputs. The goal is to facilitate prompt engineering and hypothesis testing over LLMs.
Simultaneous queries : Quickly test prompt variations and ideas by querying multiple LLMs simultaneously. This way, you can find the optimal configuration in no time.
Compare answer quality : ChainForge allows you to compare the quality of answers across different prompt permutations, models, and model settings, so you can get the best possible performance from your LLMs.
Automated evaluation metrics : Set up evaluation metrics with code or LLM-based scorers and plot the results automatically. This way you always have an overview of the performance of your models.
Multiple conversations in parallel : Conduct multiple conversations simultaneously using template parameters and chat models. This saves time and enables efficient work.
Templates and Expense Inspection : Create templates for chat messages and inspect or rate the outputs every time. This gives you full control over the conversation flow.
ChainForge goes beyond anecdotal evidence and enables robust evaluation of prompts and models with minimal effort. Filtering and grouping options help you analyze the responses, including formatted tables and exportable data.
Installation and use: How to get started with ChainForge
Installing ChainForge is very easy. You can install it locally using pip:
pip install chainforge chain forge serve
Then open localhost:8000 in a supported browser like Chrome, Firefox, Edge or Brave. Note that you will need to reset your API keys each time as ChainForge does not store them.
Alternatively, a web version with slightly limited macedonia number dataset functions is available at chainforge.ai/play. Here you will also find a handy "Share" button that you can use to generate unique web links for your LLM experiments and share them with others.
Use cases: This is where ChainForge shines in practice
The true strength of ChainForge lies in its versatile application possibilities. Three main areas stand out in particular:
1. Model selection: Find the best LLM for your needs
Choosing the right Language Model is critical to the success of your project. With ChainForge, you can easily compare the performance of different LLMs and identify the best model for your specific needs.
Imagine you want to develop a chatbot system. By comparing different LLMs in ChainForge, you can quickly find out which model provides the most natural and contextual answers. This saves you valuable time and ensures that your chatbot shines with the best possible AI support right from the start.
2. Prompt template design: Optimize your prompts
The quality of your prompts has a huge impact on the output of the LLMs. ChainForge allows you to iteratively improve your prompts and optimize them for the desired results.
Let's say you're working on a project to automatically summarize text. With ChainForge, you can test different prompt variations and evaluate them based on the summaries generated. By making gradual adjustments, you can find the optimal wording to produce precise and meaningful summaries.
3. Hypothesis testing: Understand the capabilities and limitations of LLMs
To use LLMs effectively, it is important to understand their capabilities and limitations. ChainForge allows you to test hypotheses about model behavior and gain valuable insights.
Let's say you want to find out how well an LLM can handle ambiguous or incomplete information. With ChainForge, you can create targeted test cases and analyze the model's responses. This will give you a deeper understanding of the LLM's strengths and weaknesses and enable you to use it optimally for your application.
ChainForge in comparison: What are the differences to other tools?
At first glance, ChainForge may resemble tools like Langflow and Flowise, but the focus is different. While the latter aim at developing complete applications, ChainForge focuses on evaluating and inspecting LLM outputs. The goal is to facilitate prompt engineering and hypothesis testing over LLMs.