liveBook · Manning

Rubric

As explained in Reinforcement Learning from Human Feedback

A rubric is a structured set of weighted evaluation criteria for judging the quality of a response to a specific task or prompt.

A rubric converts a prompt into weighted, testable criteria for judging generated responses. It enables reinforcement learning when answers lack directly verifiable outcomes, guiding updates toward stronger responses. section 12.6

Museum-exhibit prompt

For a request to suggest five obscure artifacts, hard rules check the count, cultural and temporal distinctness, historical significance, mysterious origins, and thematic intrigue. Principles assess organization, precision, engaging language, and thoughtful selection. section 12.6

{
  “initial_reasoning": “<brief rationale for what matters for 
    this prompt>",
  “rubrics": [
    {
      “reasoning": “<why this criterion matters>",
      “criterion": “<clear, testable criterion>",
      “weight": <integer 1-10>
    },
    ...
  ]
}

A rubric configuration records initial reasoning, criterion-specific reasoning, clear testable criteria, and integer weights from 1 to 10. section 12.6

How Reinforcement Learning from Human Feedback covers it

How deep it goes
Defines hard rules and principles, demonstrates them on a prompt, and specifies a weighted configuration schema.
Examples
Uses a museum exhibit request requiring five culturally and historically distinct artifacts with significance, mystery, and engaging presentation.
What you can do after
Apply it via reinforcement learning to evaluate and improve responses without clearly verifiable answers.
How it connects
Connects rubrics to reinforcement learning, verifiable rewards, LLM judges, synthetic data, scientific reasoning, and factuality.

Also explained in

Compare how 4 books explain rubric →

Related concepts

This explainer was generated by Manning's knowledge graph from the text of Reinforcement Learning from Human Feedback. The section links point to the parts of the book it draws on.