Eval Tool

What is an eval tool?

An eval tool is a software platform or framework designed to help developers measure and assess the quality of their AI products by running evaluations (evals) on AI outputs. These tools can range from simple custom-built solutions using existing databases like Airtable to sophisticated off-the-shelf platforms that provide built-in functionality for storing traces, running evaluations, and analyzing results.

While many specialized eval tools exist in the market, teams can start with familiar tools they already use before investing in dedicated eval platforms.

Why might teams avoid specialized eval tools initially?

Specialized eval platforms—like LangChain, Langfuse, Arize, or Braintrust—can feel overwhelming, especially when you're still learning how evaluations work. These tools come with their own perspectives and approaches to evaluation that may influence how you think about measuring quality.

More importantly, off-the-shelf eval tools measure generic metrics that may not align with your product's specific failure modes. You can buy an eval tool and it will measure many things, but those measurements aren't necessarily capturing the problems unique to your product. This is why understanding your own error patterns through manual error analysis matters more than choosing the right tool.

What tools can teams use instead of specialized eval platforms?

You can start with tools you already know. Airtable works well for storing traces and doing annotation. VS Code lets you write custom evals. Jupyter Notebooks support analysis and experimentation. These familiar tools are enough to implement a rigorous evaluation process.

Starting simple lets you learn the evaluation process without being constrained by a tool's built-in assumptions. Once you understand how evaluations work for your specific product and use case, you'll be better positioned to evaluate whether a specialized eval platform would actually help or just add complexity.

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