Golden dataset

A golden dataset is the ground truth for your evals. Each record holds an input the system will actually face (a ticket, a document scan, a question, a call transcript) and the answer a competent human agrees is correct, along with any metadata needed to score it: expected fields, acceptable variations, the right escalation decision, the source passage an answer must cite.

Building one is mostly curation. You sample from real traffic rather than inventing cases, stratify so every intent, language and document layout is represented, label with domain experts, and record disagreements. Then you freeze it, version it, and add to it deliberately as new failure modes appear in production. Cases the model was tuned on are kept separate from cases used to score it.

It is not a training set, and mixing the two inflates scores. It is also not a list of happy-path demos; a golden set that lacks blurry scans, angry customers and ambiguous questions will certify a system that fails on exactly those.

• Data lead: owns the labelling standard and is the person who decides what "correct" means for each task.
• CTO / Head of Engineering: without a golden set, every model or prompt change is an unmeasured bet.
• Product manager: the set is where product edge cases are written down explicitly, which is often the first time anyone does so.
• Compliance officer: a labelled reference set is the evidence that accuracy claims were tested on realistic data, not selected examples.

Golden datasets exist because you cannot improve what you cannot measure consistently. Without a fixed reference, two engineers evaluating the same change on different examples reach different conclusions, and quality drifts with whoever complained last. A frozen, expert-labelled set gives one number everyone trusts. The trade-off is labelling effort and the discipline to keep it honest: it must reflect real traffic, stay separate from anything used for tuning, and be refreshed as the business changes. Skimping here produces a system that passes its own tests and fails its users.

• Two hundred anonymised loan applications with checker-verified field values for a lending document pipeline.
• Historical helpdesk tickets with the reply a senior agent would have sent and the correct escalation flag.
• A golden question set of business questions with approved SQL and expected result sets for an analytics assistant.
• Transcribed hospital appointment calls in Hindi, Kannada and English with the correct booking outcome.
• Catalogue items with expert-assigned categories and attributes for a retail auto-tagging model.

Technique / practiceA practice and an asset you own. Eazyware builds the first golden set with your domain experts during ProofRun or a retrieval and knowledge engineering engagement, and hands it over with the tooling to extend it.

Eazyware service that covers it: LLM Application Development. Starting prices are on the pricing page.