Have you ever wondered how to make your AI-powered app more reliable? Scorable evals make it easy to automatically evaluate and refine your model's responses, improving performance and consistency with minimal setup.
When working with large language models (LLMs), consistency is key. You want your application to respond politely, constructively, and helpfully every time a user interacts with it. However, LLMs can vary in tone, accuracy, and usefulness depending on the context and prompt. This makes it essential to have a structured way to measure and improve responses.
That's where Scorable comes in. It provides developers with a systematic approach to evaluating AI behavior through a framework of metrics—called judges—that align with an application's goals.
To illustrate, imagine an AI job interview coach where an LLM plays the role of the interviewer. In this scenario, performance depends on how effectively the AI communicates and provides useful feedback. With Scorable, developers can create a bespoke judge that evaluates responses across dimensions such as politeness, constructiveness, and any other criteria specific to the application. These judges score model outputs and provide automated feedback, enabling faster iteration and higher-quality results without manual testing.
Getting Started
Integrating Scorable Judges into an existing application is straightforward. You can start improving your AI responses in just a few simple steps:
1. Create a judge with scorable.ai
Define the criteria you want to measure—such as tone, clarity, or helpfulness—and set up a judge in Scorable to evaluate them.
2. Update the base URL of your model provider
Point your model requests to the Scorable OpenAI-compatible proxy. Scorable supports OpenAI, Anthropic, and other major providers (full list available in the documentation).
3. Let Scorable do the rest
Once connected, Scorable automatically evaluates your model's responses, applies feedback, and improves them in real time—no changes to your app's core logic required.
In the demo video, both the original and improved responses are displayed side by side, showing exactly how Scorable refines the language model's output. The improved version demonstrates a more respectful tone, clearer structure, and more constructive feedback, all guided by Scorable' evaluation framework.
Get the code snippet from the documentation.
Taking It Further
Once the basic setup is complete, you can go further by defining your own evaluation metrics, creating custom judges for different use cases, and automating continuous improvement loops. Even a small change—sometimes just one line of code—can make your AI app significantly more reliable.
Scorable offers a practical path from experimentation to dependable AI performance, ensuring that every response better reflects the quality and consistency users expect.