Golden Dataset
What is a golden dataset?
A golden dataset is a curated collection of input-output pairs used to evaluate AI and machine learning products. Each pair defines a specific input—such as a cat image, a user query, or an interview transcript—along with its ideal or desired output, like "cat," the correct answer, or expected feedback.
The dataset serves as a benchmark for testing whether changes to a product improve or degrade its performance. Originating in classical machine learning, golden datasets provide a fixed standard for comparing different product versions.
What are the limitations of golden datasets?
While golden datasets provide a useful baseline, they come with significant constraints. They can only capture known use cases and inputs that teams have already anticipated. For new products, this creates a chicken-and-egg problem: teams want to evaluate quality before launch, but building a representative dataset requires production data that doesn't yet exist.
Additionally, golden datasets may not reflect the full complexity and variety of real-world usage. Teams need to ensure their datasets include edge cases and represent the actual inputs their products will encounter in production, not just the obvious or expected cases.
How do teams use golden datasets in evaluation?
Teams typically start by creating their golden datasets in spreadsheets, defining input-output pairs based on expected use cases. When making changes to their AI product, they run all inputs through the system and compare the outputs to the desired results, generating a score that indicates performance.
However, golden datasets work best when used alongside other evaluation methods. Teams often supplement them with additional evals based on real production traces and specific failure modes identified through error analysis.
Learn more:
- Building My First AI Product: 6 Lessons from My 90-Day Deep Dive
- AI Evals & Discovery - All Things Product Podcast with Teresa Torres & Petra Wille
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