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Rubric

As explained in AI Agents in Action

A rubric is a structured set of criteria used to specify or evaluate the quality of an agent profile or response.

A rubric is a structured set of criteria and standards for specifying or evaluating how well an agent profile or prompt meets its objectives. It replaces a simple right-or-wrong measure with criteria suited to the use case. section 9.5

Build a practical rubric by following these steps:

  • Identify the profile or agent’s purpose and objectives. section 9.5
  • Define specific, measurable criteria aligned with those objectives. section 9.5
  • Create a numerical or descriptive performance scale. section 9.5
  • Describe what strong and weak performance looks like at every level. section 9.5
  • Apply the rubric, assign criterion scores, and calculate a sum or weighted average when appropriate. section 9.5

Evaluating recommendations

A recommendation rubric can score genre, subject, and format from 1 to 5. A score of 1 means poor alignment, while 5 means excellent alignment. A second LLM prompt can evaluate each generated recommendation automatically, producing scores such as Subject: 5, Format: 5, and Genre: 4. section 9.5 section 9.6

Table 9.2 Rubric ratings
Rating
Description
1
Poor alignment: this is the opposite of what is expected given the criteria.
2
Bad alignment: this isn’t a good fit for the given criteria.
3
Mediocre alignment: it may or may not fit well with the given criteria.
4
Good alignment: it may not align 100% with the criteria but is a good fit otherwise.
5
Excellent alignment: this is a good recommendation for the given criteria.

Use the rating table to compare alignment levels consistently across evaluated recommendations. section 9.5

In Prompt flow, a rubric can become an evaluation prompt. An evaluation flow runs after a generative flow, scores results, and aggregates criteria across single or batch runs. section 9.6 section 9.8

How AI Agents in Action covers it

How deep it goes
Details objectives, criteria, scales, descriptions, scoring, and automated evaluation, but does not specify a universal rubric.
Examples
Scores recommendations by genre, subject, and format using a one-to-five alignment scale.
What you can do after
Apply it via Prompt flow to score recommendations automatically or establish a manual evaluation baseline.
How it connects
Connects rubrics with agent profiles, prompts, grounding, prompt engineering, Prompt flow, and LLM evaluation.

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This explainer was generated by Manning's knowledge graph from the text of AI Agents in Action. The section links point to the parts of the book it draws on.