Why Language Models Need a Lesson in Education

Stephanie Kirmer, a staff machine learning engineer at DataGrail, adapts her experience as a former professor to address the challenge of evaluating LLMs in production. She proposes a robust methodology using LLM-based evaluators guided by rigorous, human-calibrated rubrics to bring objectivity and scalability to the subjective task of assessing text generation quality.

August 19, 2025 4 min read

Why Language Models Need a Lesson in Education — source: https://www.youtube.com/watch?v=kWhP4R1vHU8

The Core Challenge: Evaluating Subjective LLM Output

Evaluating Large Language Models (LLMs) for production presents a unique challenge compared to traditional machine learning. While standard models have clear metrics like accuracy or error rates, the free-text output of LLMs is inherently subjective. The common approaches to evaluation each have significant drawbacks:

Human-in-the-loop evaluation is high-quality but slow and expensive, making it difficult to scale.
Using another LLM as a judge can feel like “LLMs all the way down,” raising questions of trust and objectivity.
Built-in metrics within evaluation frameworks, such as “correctness” or “conciseness,” are often vague and lack transparency about what they are truly measuring.

To deploy LLMs with confidence, we need a reliable way to ensure they perform as expected and avoid catastrophic mistakes.

A Solution from Academia: The Rubric

The proposed solution adapts a tool from education: the rubric. Just as a professor uses a rubric to define success for a student’s essay, a machine learning team can use one to clearly and explicitly define what a successful LLM response looks like for a given task.

A well-designed rubric serves two key purposes:

Defines Expectations: It clearly outlines the criteria for a successful output, ensuring everyone on the team understands the goal.
Enables Objectivity: It breaks down a complex, subjective task into measurable components, making the evaluation process as objective as possible.

How to Create and Structure a Rubric

Creating a strong rubric begins with a deep understanding of the task and the end user’s needs. It’s crucial to know what a “good” answer looks like from the perspective of those who will consume the output.

The process involves several steps:

Define the Goal: Start with a clear, high-level objective. For example: “The LLM should provide friendly, professional, and complete responses to users asking for product details.”
Identify Measurable Components: Break down the goal into distinct, measurable traits. In the example above, the components are: Friendliness, Professionalism, Completeness, and On-Topic.
Describe Performance Levels: For each component, define what different levels of performance look like and assign them point values. This transforms qualitative traits into a quantitative score.

For instance, for the Completeness component:

0 points: The response fails to address the user’s question.
1 point: The response partially addresses the question but has significant gaps.
2 points: The response answers most of the question with only minor gaps (a “passing” grade).
3 points: The response perfectly and thoroughly addresses all aspects of the user’s question.

A critical test for a rubric is whether a human can convincingly and consistently assign a score. If you find yourself unsure whether a response is a “1” or a “2,” the descriptions for those levels need more clarity and detail.

The Validation and Calibration Loop

Once a rubric is drafted, it’s not ready to be handed to an LLM evaluator. It must be validated and calibrated to ensure the LLM’s judgment aligns with your own.

This process works as follows:

Create a Validation Set: Generate or manually write a sample of texts that are representative of the task LLM’s potential output. Crucially, this set must include examples of varying quality—some excellent, some terrible, and some that fail on different criteria (e.g., one is rude but complete, another is polite but off-topic).
Grade Manually: You, the human expert, grade the entire validation set using the rubric. This creates your “ground truth.” This step also helps you identify and refine any “fuzzy spots” in the rubric itself.
Have the LLM Grade: Pass the same validation set and rubric to an out-of-the-box LLM evaluator and ask it to assign scores.
Analyze and Iterate: Compare the LLM’s scores to your ground truth scores. Where there are discrepancies, refine the prompt you provide to the evaluator LLM. The goal is to use prompt engineering to “train” the evaluator LLM to apply the rubric in the same way you do, effectively making it your teaching assistant (TA).

From Calibration to Production

After the calibration process, you have an evaluator LLM that you can trust to score your task LLM’s output reliably.

Continuous Monitoring: This evaluator can be deployed to continuously score the task LLM’s output in production, providing quantifiable metrics to monitor performance, detect drift, and flag outliers. To manage costs, this can be done via sampling or spot-checking.
Maintain Calibration: Periodically, you should manually grade a small sample of new outputs to ensure the evaluator LLM remains aligned with your judgment.

Advanced Applications

This evaluation framework unlocks several advanced possibilities:

Improved Prompt Engineering: The quantitative scores from the evaluator provide rapid feedback for iterating on and improving the prompt for the primary task LLM.
Quality Gating: The scores can act as a real-time quality filter, preventing low-scoring responses from reaching an end-user or proceeding down an agentic pipeline.
Reinforcement Learning from AI Feedback (RLAIF): The setup provides the foundation for RLAIF, where the evaluator LLM’s feedback is used to systematically fine-tune the task LLM.