LLM-as-a-Judge (LaaJ) is a method for evaluating the outputs of large language models (LLMs) by using another LLM to assess the quality of AI-generated text against predefined criteria. Evaluating LLM performance can be challenging due to the models’ complexity and the varied requirements of different tasks.

LLM outputs vary depending on the prompt and the task’s nature. As a result, the criteria used to evaluate one response may not apply to another. For example, a model may generate a response that is factually incorrect or fails to address the user’s question, but later outputs may not repeat the same issues. 

Using LaaJ introduces consistency by applying a standardized, scalable framework for reviewing model performance. For enterprise applications in sectors such as finance, healthcare, and retail, this can help validate AI outputs for business use, reduce risk, and increase trust in automated systems.

Why use LLM-as-a-Judge?

LLM-as-a-Judge (LaaJ) offers an efficient alternative to human-based evaluation of large language model (LLM) outputs. While traditional performance assessments rely on human reviewers — a time-consuming and resource-intensive process — LaaJ enables faster, more scalable evaluations. 

It applies a consistent set of criteria, helping to reduce variability and subjectivity in the review process. This is especially valuable in industries like finance, where large volumes of reports and summaries must be evaluated for accuracy and clarity.

Generating outputs with LLMs is inherently complex and may occasionally result in factual or logical errors. A structured, criteria-driven approach like LaaJ improves quality assurance by validating responses against specific standards. 

Unlike generating original content, evaluating responses is a more constrained task for the model, making it more reliable when applying rules-based assessments. For instance, a model can be instructed to judge whether a response is coherent or factually accurate. Clearly defined evaluation criteria enhance consistency and reduce the influence of human bias.

How does LLM-as-a-Judge work?

LLM-as-a-Judge (LaaJ) follows a structured evaluation process often referred to as DDPA — define, design, present, and analyze.

Define

The first step is to define the judging task. This involves determining what type of content or output the LLM is expected to evaluate. Judging criteria might include the quality of the response, factual accuracy, coherence, or how well the output aligns with a given evaluation framework. The define stage establishes the foundation for the entire evaluation process.

Design

In the design phase, a prompt is crafted to guide how the LLM should assess the outputs. An effective prompt typically includes:

  • Relevant context — background information to help the model understand the task.
  • Evaluation criteria — specific qualities to assess, such as correctness, clarity, or relevance.
  • Formatting rules — instructions on how to structure the evaluation, such as score ranges, text labels, or narrative feedback.
  • Reference materials — any additional documents or examples the model may need to perform an informed evaluation.

Present

At this stage, the model is presented with the prompt and the content to evaluate. Inputs may include single or multiple pieces of content, for instance, alternative responses for comparison. These inputs can be text, code, or other supported formats. If the model needs further context to perform the task accurately, it should also be included.

Analyze

Finally, the model performs the evaluation. It interprets the prompt and context, applies the predefined criteria, and generates a judgment. Depending on the design, the output may be a qualitative assessment (e.g., descriptive feedback) or a quantitative score (e.g., a numerical rating or categorical label). This structured evaluation helps ensure consistent, repeatable assessments across large volumes of content.

What are common modes of LLM-as-a-Judge?

LLM-as-a-Judge can be implemented using three common modes:

Single output scoring (no reference)

In this mode, the judge LLM evaluates a single output using a scoring matrix — a structured framework that defines how to assess quality, coherence, or accuracy. The model applies these criteria to assign a score to the output. However, without a reference example for comparison, this method can sometimes lead to inconsistencies in scoring due to subjective interpretation.

Single output scoring (with reference)

This approach is similar to the no-reference method but includes a reference output. Providing a reference helps the judge model apply scoring more consistently, as it can directly compare the candidate output against a predefined standard. This method can improve alignment and reduce variance in evaluations.

Pairwise comparison

In a pairwise comparison, the judge LLM is presented with two different outputs and asked to determine which one is better based on custom evaluation criteria. These comparisons may assess dimensions such as relevance, helpfulness, factual correctness, or level of detail. Pairwise comparison can be used to evaluate differences across models, prompts, or other system configurations, offering a more nuanced view of output quality. 

What are common LLM-as-a-Judge evaluation metrics?

LLM-as-a-Judge (LaaJ) uses several key metrics to assess the quality and reliability of model outputs. These metrics help ensure responses are accurate, relevant, and aligned with business requirements.

Relevance

Measures whether the model’s response addresses the prompt or user query. In other words, does the output meaningfully answer the question? Relevance can be evaluated manually or through automated scoring systems.

Hallucination detection

Detects whether the model has included false or fabricated information — content that is not grounded in the source material or reference data. Identifying hallucinations is critical in domains like healthcare and finance, where misinformation can have serious consequences.

Question-answering accuracy

Evaluates how correctly the model answers general or domain-specific questions. This is typically measured against ground truth, a verified set of facts used as a benchmark. High accuracy is especially important in applications like financial disclosures or medical documentation, where incorrect responses can carry significant risks.

Contextual relevance

Assesses how well retrieved or referenced information supports the response. This is especially important when using retrieval-augmented generation (RAG) models, which incorporate external documents. The goal is to confirm that outputs are relevant and appropriately grounded in the retrieved context.

Faithfulness

Measures how accurately the model’s response reflects the source content it was given. A faithful response avoids adding information not present in the reference material, helping to reduce hallucinations and preserve trust in automated systems.