LLM-as-a-judge (LLMAJ) has emerged as a powerful tool for evaluating and validating the outputs of generative models. Closely observed and managed, the practice can help scalably evaluate and monitor the performance of Generative AI applications on specialized tasks.  

However, challenges remain. AI judges must be scalable yet cost-effective, unbiased yet adaptable, and reliable yet explainable. LLMs (and, therefore, LLM judges) inherit biases from their training data. Sometimes these biases favor verbosity or certain stylistic features, and can sometimes offer opaque or misleading reasoning in their decisions. Addressing these issues is critical to ensuring trustworthy AI evaluations.  

In this article, we’ll explore how enterprises can leverage LLM-as-a-judge effectively, overcome its limitations, and implement best practices.

Let’s dive in.

What is LLM-as-a-judge?  

At its core, LLM-as-a-Judge refers to the use of large language models to evaluate, compare, and validate outputs generated by AI models, including other LLMs. In short, it’s one of many tools for LLM evaluation. It replaces or augments the use of human annotators, which frontier model companies have employed by the hundreds or thousands to rate or rank LLM outputs.

What is the basic form of an LLM-as-a-judge?

At its most basic level, an LLMAJ system consists of three parts:

1. Input data: the output to be judged.
2. A prompt template: the frame that holds the output to be judged and instructions on how to judge it.
3. An LLM: the neural network that takes in the final prompt and renders verdict.

More advanced systems could incorporate variable prompt templates, multiple LLMs, or multiple inputs to be judged against each other.

How do you teach an LLM to judge?

An LLM’s ability to evaluate an input depends on how a prompt template structures the task. A well-designed prompt ensures the model follows clear evaluation criteria, reducing randomness and improving consistency.

A typical LLM-as-a-judge prompt template includes:

• The task definition: “Evaluate the following contract clause for ambiguity.”
• Evaluation criteria: “Rate clarity on a scale from 1 to 5, considering legal precision.” Alternatively: “which of these two chatbot responses best aligns with company policy?”
• Justification request: “Explain why this response was rated higher.”

These prompts also typically dictate that the LLM return its results in JSON structure. This makes it easier for researchers to compile and assess verdicts at scale. Some model providers also equip their APIs with “structured output” options that eliminate the need to explicitly request a JSON format in the prompt.

By structuring the prompt this way, enterprises can make the LLM’s judgment more reliable, interpretable, and aligned with business needs.

# Task

Your task is to evaluate the response to a user question based on compliance with internal policy.

You will be provided with:

* User question 

* Model response

* Relevant policy sections

Carefully analyze the response against the provided policy sections and evaluate compliance - is the response compliant, or is there non-compliance?

You must explain your judgement by providing a justification with citations to the appropriate sections within the provided policy.  

The judgement should be a binary label:

* True - The response is compliant.

* False - The response is noncompliant.

# Content

Question: {question}

Response: {response}

Relevant policy: {policy}

How do you ground your AI judging system?

When building an LLM-as-a-judge, best practices dictate that data scientists should work closely with subject matter experts (SMEs). Data scientists should ask SMEs to label a small amount of ground truth.

For each record, the SME should provide:

1. The label for each criteria, which could come in any form the data scientist deems necessary. This could include a rating on a Likert scale, a binary approval metric, or a selection between multiple options. Often, these systems will “judge” the LLM output along many aspects, which may include factual accuracy, tone, an overall rating, or anything else important to the project.
2. A justification for each label, which explains why the SME applied their chosen label.

Data scientists and SMEs use this ground truth to guide iterations on the LLM-as-a-judge prompt template. This takes several forms. 

The team may embed some of the SMEs labels and explanations directly in the template as a form of prompt engineering known as “few shot learning.” 

Once the team has built its initial prompt template, data scientists compare the LLM-as-a-judge’s labels to those provided by the SME to find where they diverge. This, combined with comparing the AI judge’s logic to the SME’s logic, allows data scientists to predict and experiment with the best ways to fine-tune the prompt before testing a new version.

In some cases, the SME may want to update their logic to reflect blindspots revealed by LLM justifications. This is particularly true for SME-provided reasoning that data scientists choose to use in prompt templates.

The team continues this process until the LLM-as-a-judge reliably agrees with the project’s SME’s.

Why should you ask AI judging systems to justify their verdicts?

Asking an LLM to justify its response may feel counterintuitive; the evaluation process cares only about the verdict, not the justification, right?

Not quite.

The justification aspect of the ground truth and prompt template serve two purposes:

1. Better labels: researchers have found that asking an LLM to explain its ratings “consistently improves the correlation” between LLM labels and human labels.
2. Better iteration: The goal of iterating on the LLM-as-a-judge prompt is to align the judge’s assessment with that of SMEs. When the LLM and SME disagree, comparing their justifications informs how to adjust the prompt template.

Why “slice” data when developing an LLM-as-a-judge?

Project participants should keep a keen eye on clusters of similar tasks or task features when undergoing any AI application iteration process. At Snorkel AI, we call these “data slices.”

Data slices allow data scientists to zoom in on where a model—or an LLM-as-a-judge system—fails most often. Instead of looking at SME disagreement record-by-record, data slicing allows data scientists to see at a glance that their “judge” has struggled with particular subtasks, such as questions in Spanish or requests to cancel an account.

This enables more targeted and efficient adjustments to the judge template.

LLM-as-a-judge vs LLM-assisted labeling

What’s the difference between LLM-as-a-judge and LLM-assisted labeling? LLM-assisted labeling uses the LLM’s embedded understanding of language to generate labels. LLM-as-judge is a specific application of LLM-assisted labeling that uses LLM’s to evaluate and improve generative AI (GenAI) outputs.

Researchers and enterprise data scientists have used LLMs to help scale data labeling efforts since shortly after the introduction of BERT. Snorkel users employ well-prompted LLM’s as a foundational labeling tool on proprietary enterprise data within the Snorkel AI data development platform, and researchers across industry and academia continue to develop new LLM-assisted labeling tools, such as ALFRED.

What are some LLM-as-a-judge challenges and considerations?

Despite its advantages, the LLM-as-a-judge paradigm presents a few challenges:

• Bias and fairness: LLMs inherit biases from their training data, sometimes skewing evaluations. These biases could be influenced by factors such as the order of answer options, the inclusion of citations (even if fabricated), or the length of the judged response. 
• Interpretability: Understanding the rationale behind LLMs’ judgments can be complex, necessitating transparent evaluation criteria. Additionally, researchers have found that the “judge’s” rationale can sometimes be “adversarially helpful.” They may justify incorrect answers and potentially convince human reviewers that they are correct.
• Ethical and legal Implications: Relying on LLMs for evaluative tasks raises ethical questions about accountability and the potential reduction of human oversight. In legal contexts, for instance, the use of AI in decision-making processes must be carefully managed to avoid unintended consequences.

Even considering these concerns, LLM-as-a-judge pipelines—properly crafted and monitored—represent a robust tool for evaluating LLM outputs.

How did biases in GPT-4 cause AlpacaEval to change?

In 2024, Snorkel researchers launched an experiment using direct preference optimization (DPO) to fine-tune a better-performing LLM. To test the results, they submitted their model to the AlpacaEval leaderboard, which their model quickly topped. 

But their results appeared too good to be true.

AlpacaEval employs an LLM-as-a-judge setup, prompting the LLM to choose the better response between two options. The prompt offers a fixed response created by GPT-4 and one generated by the model currently under examination. The pipeline repeats this process and awards a score according to the percentage of time it chose the evaluated model’s response over the cached and reused response from GPT-4.

Upon investigation, our researchers discovered that the LLM-as-a-judge system favored longer responses over quality, indicating a bias towards verbosity. This unintended bias led to the system favoring lengthier outputs, inadvertently skewing the evaluation process. 

Through fine-tuning, our researchers created an LLM that produced longer responses, unaware of this quirk. They had accidentally gamed the system. 

They raised this concern with AlpacaEval’s administrators, who quickly updated their evaluation criteria to adjust win-rates according to model response length. This revised the Snorkel model’s standing downward, which our researchers felt more accurately reflected its actual performance. Data scientists working on other LLM-as-a-judge projects could modify results in a similar fashion, or try to adjust outputs through further prompt engineering.

How to build scalable and cost-effective AI judging systems for production?

​LLM-as-a-judge has proven effective for evaluating AI outputs in offline batch-processing scenarios. However, deploying LLM-as-a-judge systems in real-time LLM applications presents significant challenges due to inherent latency and resource demands.​

Each invocation of an LLM-as-judge requires running hundreds or thousands of tokens through a multi-billion parameter LLM. This introduces latency that can disrupt the user experience. Additionally, the larger models typically used in LLM-as-judge systems demand substantial computational resources, making real-time deployment technically and financially impractical.​

Production LLM-based applications often employ smaller, specialized guardrail models as an AI judging system. These models monitor and filter AI outputs efficiently, providing immediate feedback.

Enterprise data scientists can transfer the effectiveness of the LLM-as-judge system into a smaller format, however, through a process known as LLM distillation. This process trains a small “student” model to replicate a large “teacher” model’s performance on one or more specific tasks, resulting in a more efficient model suitable for real-time deployment. 

LLM-as-a-judge offers a powerful and scalable approach to evaluating generative AI outputs, assisting enterprises and researchers in aligning models with user expectations more efficiently. By replacing or supplementing traditional human labeling processes, LLM-as-a-judge can dramatically reduce costs, accelerate iteration cycles, and improve consistency in AI evaluation.

However, as demonstrated by challenges in bias, interpretability, and scalability, this approach is not without its limitations.

To fully realize the potential of LLM-as-a-judge, organizations must adopt best practices, including careful prompt engineering, grounding evaluations with expert-verified ground truth, and maintaining human oversight for edge cases. 

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Frequently asked questions about LLM-as-a-judge