Vs ground truth
Ground truth is the correct label or outcome; a golden dataset is a curated collection of such cases with reuse rules.
Definition
What is a golden dataset for LLMs?
A golden dataset is a set of example requests with answers or actions that reviewers have checked and approved. Teams use it to test models, catch new failures, and give agents examples to follow.
Golden dataset meaning for LLMs
A golden dataset contains selected requests and answers that a team has checked for a specific task. It can support model tests, regression checks, or a library of examples the agent retrieves while responding.
The number of records doesn't tell you whether the set is useful. Reviewers need to know why each answer is correct and which tasks it applies to.
Golden dataset vs training data vs eval logs
Training data changes the model's weights. Evaluation logs record what the system did. A golden dataset records the expected answer or actions for selected cases, with enough context to check why they are correct.
For agents that use tools, the expected result may include required checks and tool calls as well as the final reply. This sequence is often called a trajectory.
Where production traces fit
Production traces are logs of real messages, tools, and results. Reviewers can use them to build examples by recording the correct reply or actions and explaining why they approved them.
Using real cases helps the dataset reflect the mistakes your users encounter. Synthetic examples can add coverage, but they still need review.
See the commercial approach on golden datasets for AI agentsLearn the production-first build path for golden datasets from production traces
Common questions
What is a golden dataset for LLMs?
It is a set of example requests paired with checked, approved answers or actions. Each record should say who approved it, which task it applies to, and any restrictions on reuse.
Is a golden dataset the same as a benchmark?
Not necessarily. Public benchmarks are shared test sets. A golden dataset is usually specific to a team's policies, tools, and goals. A checked benchmark can serve as a golden set for the capability it tests.
Can raw production logs be a golden dataset?
Not without review. Logs show what happened. Reviewers need to check the correct reply or actions and record where the example applies before it becomes part of a golden dataset.
How do golden datasets help AI agents at runtime?
The agent can look up an approved example when it gets a similar request and include it in the prompt. This lets a team reuse corrections without retraining for every exception.
How do you start building one?
Pick one workflow, sample recurring failures from production traces, write human-approved replacements, store them with version and scope, then measure whether retrieval improves outcomes. See our how-to and service pages for the full path.
Related concepts and examples
These terms describe different uses of data. A reviewed example may be useful for several of them, but test cases must stay separate from training.
Vs benchmark
Benchmarks are shared evaluation sets; golden datasets are usually team-specific and tied to your tools and policies.
Vs training or validation sets
Training changes weights; validation helps choose settings. A golden dataset contains reviewed reference answers and can support testing or retrieval.
Vs logs
Logs record what happened. A golden dataset records the checked, expected result for selected cases.
Example shapes
Classification labels, generation references, RAG answer-plus-citation pairs, and agent trajectories with required tool steps.
Free template
Inspect a filled golden-dataset example
Compare JSON and CSV versions of a made-up example, including its source, correction, approval, and rules for reuse.
This template uses made-up examples, not client data. Adapt it to your policies, privacy requirements, and approval process.
What a useful golden dataset contains
Include the request and context, the approved reply or actions, why they're correct, who approved them, and where the example may be reused.
Representative scenarios
Include recurring or costly request types and difficult cases, rather than only examples that are easy to get right.
Approved ideal outputs
Human-reviewed responses or action paths with critique, not only pass/fail labels.
Governance metadata
Workflow scope, version, approval status, and restrictions so examples can be audited or disabled.
Limitations
- Public benchmarks rarely encode your tools, policies, or escalation rules.
- Logs need review before they can become approved examples.
- Unversioned spreadsheets let stale answers enter evaluation or retrieval.
- Approved examples can also support retrieval, as long as you define where they may be used.
How we test changes
We review real conversations with your team and agree what the agent should do differently. Approved corrections can become training data, test cases, or examples the agent looks up. We compare each change with your best existing setup on separate test cases, then check for new failures in the software you plan to deploy.
References:
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