Human annotation

In this guide, we’ll explore how to create custom human evaluators by defining a simple Conciseness Rating on the likert scale (1-5), which allows users to manually grade outputs on a custom evaluation criteria.

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Creating the evaluator

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UI Walkthrough

Use the following UI walkthrough alongside the guide below to create a custom Python evaluator in the HoneyHive console.

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Navigate to the evaluator console

Navigating to the Evaluators tab in the left sidebar. Click Add Evaluator to create a new evaluator and select Human Evaluator.

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Define the criteria

Here, you can provide a description of the evaluation criteria to your annotators. This description will be available to any reviewers when evaluating individual events or sessions.

For this example, we will provide a detailed criteria for grading Conciseness. See below.

1. Relevance: Is the response directly related to the prompt without unnecessary details?
2. Clarity: Is the message clear and easily understandable?
3. Word Economy: Are unnecessary words, phrases, or sentences eliminated?
4. Precision: Does the response use precise language without being vague?
5. Elimination of Filler: Are redundant or filler words removed?
6. Logical Flow: Does the response follow a logical sequence without unnecessary jumps?
7. Brevity vs. Completeness: Is the response concise while still covering all necessary points?
8. Consistency: Does the response maintain consistent conciseness throughout?
9. Engagement: Does the response keep the reader's interest despite its brevity?
10. Overall Impact: Does the response effectively convey the message concisely?

We’ll simply copy and paste the above code in the evaluator console. See below.

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Configuration and setup

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Configure return type

We currently support the following return types:

1. Numeric: Allows grading on a scale of 1-n, where n is the Rating Scale
2. Binary: Allows grading on a 👍/👎 scale
3. Notes: Allows providing free-form text as feedback

Since our evaluator in this example needs to return value between 1-5, we’ll configure it as Numeric and define Rating Scale as 5.

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Configure passing range

Passing ranges are useful in order to be able to detect which test cases failed in your evaluation.

We’d ideally want the model to score 4 or 5, hence we’ll configure this as 4 to 5. This’ll allow us to account for slight errors in judgement made by the evaluator.

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Using the evaluator

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Annotating logs

All evaluators defined here will automatically appear in the trace view under the session object. You can simply provide feedback on each session or any events / spans within a session. See below.