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The Preset Evaluator Examples sample project is a pre-populated Splunk Agent Observability project designed to help you understand how out-of-the-box evaluators behave on real-looking examples. This project includes curated evaluation examples (within Agent Streams and Experiments) with evaluator scores and explanations so you can quickly compare high-scoring vs. low-scoring cases.
The fastest way to explore is to start from an evaluator page in the docs, then look for the corresponding examples inside Preset Evaluator Examples.

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How it’s organized

  • Curated examples: You’ll find pre-populated data that demonstrates how evaluators score different cases.
  • Drill-down friendly: Open rows to compare the input/output with the evaluator explanation side-by-side.
  • Designed for contrast: Use sorting and filtering to compare strong vs. weak examples for the same evaluator.

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What to look for

  • Score distribution: Look at the range of scores across traces to calibrate what “good” and “bad” looks like for that evaluator.
  • Explanations: Open a handful of rows and read the evaluator explanation carefully — it’s often the quickest way to learn the rubric the judge is applying.
  • Edge cases: Pay special attention to traces that surprise you (high score when you expected low, or vice versa). These are the best starting points for refining prompts, tools, or evaluation criteria.
  • Evaluator interplay: Some failures show up across multiple evaluators. Use the examples to learn when you should monitor a second evaluator alongside your primary one.

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A quick tour

1

Pick one evaluator you care about

Start from the relevant evaluator documentation page, then jump into the corresponding examples in Preset Evaluator Examples.
2

Review the best and worst traces

Sort by the evaluator value and open a few of the highest-scoring and lowest-scoring rows.
3

Extract reusable patterns

Keep track of 2–3 patterns that correlate with strong scores (and 2–3 patterns that correlate with weak scores). These become concrete hypotheses you can test in your own app.
4

Apply it to your own Agent Stream

Enable the same evaluator on your own Agent Stream, then see whether the patterns you observed hold up on your real traffic.

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Jump into evaluator documentation

RAG evaluators

Explore evaluators focused on answer quality and grounding.

Agentic AI evaluators

Explore evaluators for multi-step agents, tool use, and trajectories.

Safety and Compliance evaluators

Explore evaluators focused on harmful content and prompt attacks.

Text-to-SQL evaluators

Explore evaluators for query correctness, adherence, efficiency, and safety.

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Next steps