What is an LLM-as-a-judge? When to use it (and when to use deterministic evals)

LLM applications generate large volumes of outputs, and teams need a reliable way to evaluate the output quality before trusting it in production. Manual review can catch obvious issues, but it does not scale as traffic grows, and traditional monitoring metrics such as BLEU or ROUGE focus on text similarity rather than whether a response is correct, relevant, grounded, or safe.

LLM-as-a-judge enables teams to evaluate response quality at scale by using one LLM model to assess another against clearly defined criteria and return structured scores or pass/fail decisions. Instead of checking wording alone, LLM-as-a-judge evaluates meaning, factual alignment, structure, and tone, allowing teams to consistently measure large volumes of outputs without depending on manual review.

In this guide, we explain how LLM-as-a-judge works, how to design reliable judge prompts, and how to integrate it into real evaluation workflows.

What is LLM-as-a-judge and how does it work?

LLM-as-a-judge is a technique in which one language model evaluates the output of another using clearly defined criteria. Instead of relying on a human reviewer to read each response and assign a grade, the judge model applies those criteria programmatically and returns structured results such as scores, labels, or pairwise preferences.

An LLM-as-a-judge setup consists of three coordinated components that work together to produce reliable evaluation results.

Evaluation criteria: The team defines explicit quality standards that describe what a strong response should achieve for the given use case. These criteria may include correctness, relevance, groundedness, tone, completeness, or safety. Clear definitions prevent ambiguity and guide consistent scoring.

Evaluation inputs: The judge receives the material required to make a decision, typically including the original user input and the model's output. In some cases, it also includes a reference answer, supporting documents, or source context to help the judge assess factual alignment and grounding.

Structured response format: The judge returns its decision in a predefined format, such as a numeric score, a pass/fail label, or a pairwise preference between two outputs. Structured outputs allow teams to aggregate results, compare versions, and integrate scoring into automated workflows.

Structured response format works because instruction-tuned models learn to recognize patterns of helpfulness, accuracy, and structure during training. When evaluating another model's output, the judge applies those learned patterns to determine whether the response meets the defined criteria. Evaluation relies on recognition rather than open-ended generation, which generally yields more stable, consistent behavior.

Where LLM-as-a-judge fits in the evaluation stack

LLM-as-a-judge operates within a layered evaluation system that combines deterministic checks, model-based scoring, and human oversight. Code-based checks validate structural requirements such as format compliance, length limits, and schema rules, while LLM-as-a-judge evaluates qualitative dimensions that code cannot measure reliably, including helpfulness, tone alignment, factual grounding, and whether the response directly answers the question. Human review completes the process by sampling judge outputs to confirm alignment and monitor for drift over time, ensuring that structural accuracy, subjective quality, and long-term reliability are all covered.

LLM-as-a-judge evaluation patterns

Most LLM-as-a-judge implementations use one of the patterns below, chosen based on whether the goal is measurement, comparison, gating, or risk reduction.

Chain-of-thought prompting strengthens all four evaluation patterns by instructing the judge to reason through each criterion before assigning a final score or label. Research such as G-Eval shows that adding structured reasoning improves correlation with human judgments because judges explicitly evaluate each quality dimension rather than relying on surface patterns. Structured reasoning improves scoring consistency and yields results teams can trust in regression testing and release decisions.

When to use LLM-as-a-judge: Best use cases and strengths

LLM-as-a-judge performs best when the evaluation criteria are subjective but describable in natural language, and when the volume of outputs exceeds what human reviewers can process within a development cycle.

Tone, style, and helpfulness are hard to reduce to rules but easy to describe in a prompt. A criterion like "the response should be empathetic without being patronizing" or "the answer should use plain language for a non-technical reader" gives the judge clear standards to apply consistently across large datasets.

Relevance and coherence evaluation asks whether the output actually addresses the user's question. A user who asks about cancellation policies should not receive a generic FAQ dump, and an LLM judge can detect that mismatch because it understands the semantic relationship between the question and the response, something a keyword check would miss.

Regression testing after prompt or model changes is where LLM-as-a-judge becomes operationally valuable. Modifying a system prompt, swapping a model, or changing retrieval parameters can introduce regressions across hundreds of test cases. Human reviewers cannot handle that volume of evaluations within a development cycle, but an LLM judge can complete the same assessment in minutes, giving teams confidence to iterate faster.

Safety and compliance screening uses pass/fail judges to flag toxic content, PII leakage, or responses that violate content policies. Because the judge operates independently of the production model and uses a separate evaluation prompt, it can catch outputs that the production model's own safety training misses.

When LLM-as-a-judge fails: Bias, limitations, and known pitfalls

LLM judges inherit the limitations of the models that power them, and several failure modes appear consistently across implementations.

Factual verification without reference context is the most common failure. Asking a judge, "is this response factually accurate?" without providing source material gives it no ground truth to compare against. The judge defaults to assessing whether the response sounds plausible, and confident, fluent hallucinations often receive high scores as a result. Providing source documents or expected answers as part of the judge prompt eliminates unreliable scoring.

Specialized domain evaluation, such as medicine, law, or finance, is where LLM judges are less reliable. These domains require detailed subject-matter expertise and familiarity with edge cases that general-purpose models do not consistently handle. The judge may overlook subtle but critical mistakes, such as incorrect drug interactions, misapplied legal standards, or flawed financial assumptions. In these settings, a response can appear coherent and well-structured while still being materially wrong.

Position bias consistently affects pairwise comparisons. Judges may prefer one response simply because of its placement rather than its content. Even small changes in label formatting or presentation order can influence outcomes. If responses are always shown in the same order, the evaluation can systematically favor one side. Randomizing order across runs and comparing results in both directions helps detect and reduce this bias.

Verbosity bias causes judges to assign higher scores to longer responses, even when a shorter answer would be more appropriate for the context. This creates an unintended incentive for models to add filler content that improves evaluation scores but worsens the user experience. Length should never be rewarded unless it is explicitly tied to quality criteria.

Self-enhancement bias occurs when a model evaluates outputs from its own model family. A judge may implicitly favor responses that resemble its own training patterns or stylistic norms. Self-enhancement bias can distort cross-model comparisons when one of the systems being evaluated is closely related to the judge. The evaluation may reflect stylistic alignment rather than objective quality differences. Using a neutral judge model or rotating judges across providers helps reduce self-enhancement bias.

Reward hacking is a form of evaluation manipulation in which a model learns to optimize for scoring rules rather than actual quality. If the judge rewards longer answers, the model may generate longer responses simply to increase its score. The added length improves the metric but not the output's usefulness. Models can also exploit weaknesses in the judge prompt. Hidden instructions or formatting tricks can influence how the response is evaluated. When prompts are not protected, the scoring system itself becomes vulnerable.

Non-determinism means the same output can receive slightly different scores across evaluation runs because the judge is a probabilistic system. You might evaluate a response today and see a small change in score tomorrow. Score variation across runs is expected, but it affects how results should be interpreted. Running the judge multiple times and averaging the scores reduces scoring inconsistency. Teams should also monitor score variance, since small changes near a threshold may reflect randomness rather than a meaningful shift in quality.

How to build a reliable LLM-as-a-judge pipeline

No single technique prevents every failure mode. A reliable pipeline uses multiple safeguards, each addressing a different risk.

Start with a calibration set

Begin with a set of human-labeled examples that represent common scenarios and edge cases. Run the judge on this dataset and compare its decisions with human judgments. If agreement is low, revise the prompt or change the judge model before expanding usage. As the system matures, grow the dataset to reflect the variety of inputs seen in production.

Write judge prompts with measurable criteria

Judge prompts should define clear and observable standards since broad instructions produce inconsistent scoring. Instead of asking the model to evaluate quality in general terms, define what quality means. Require the response to address all parts of the question, rely only on the provided context, and avoid unsupported claims. Clear criteria produce more stable evaluations, requiring the judge to reason through each criterion before scoring, further reducing variance.

Provide reference answers when available

When a correct or expected answer exists, include it in the evaluation to help the judge compare claims against a known reference rather than relying on surface plausibility. Reference material is especially important for factual checks, where fluent but incorrect answers can otherwise pass.

Run adversarial tests regularly

Evaluation systems should be tested deliberately for weaknesses. Change the order of responses in pairwise comparisons to detect position bias, and submit incorrect but well-written answers to confirm that factual errors are caught. Vary the output length as well to ensure longer responses are not rewarded without justification. Regular stress testing helps surface issues before they affect production decisions.

Aggregate across multiple runs

Language model judgments vary slightly across runs, so a single score may not tell the full story. Running the same evaluation multiple times and averaging the results reduces this variability, producing a more stable signal. Tracking score variance also provides context. Large fluctuations suggest uncertainty and may indicate that a case deserves human review.

Maintain ongoing human spot checks

Human review remains necessary even in automated pipelines. Periodically sample judge outputs and compare them with independent human scores to monitor alignment. If disagreement increases over time, the evaluation criteria may need refinement, or production behavior may have shifted. Ongoing comparison prevents slow drift from going unnoticed.

Combine deterministic and LLM-based evaluation

Deterministic checks should handle everything that can be measured directly, including format, schema compliance, and required fields, as these checks are inexpensive and predictable. LLM-as-a-judge should focus only on subjective dimensions that require language understanding. Separating deterministic checks from LLM-based scoring improves reliability and reduces the impact of judge errors.

LLM-as-a-judge in CI/CD: Automating evaluation for prompt and model changes

The value of LLM-as-a-judge increases when evaluations run automatically on every code change. Prompt edits, model swaps, and retrieval adjustments often introduce subtle regressions that manual review will not catch.

When evaluations are part of the test suite, every pull request that changes prompts, models, retrieval logic, or post-processing triggers a full run against the evaluation dataset. The evaluation results should report which test cases improved, which regressed, and by how much, so reviewers can make informed merge decisions rather than relying on spot-checking a handful of outputs.

Configuring regression gates: Regression gates block deployments when scores fall below defined thresholds. If a factuality scorer requires 85% or higher and a prompt change reduces it to 78%, the gate prevents the change from reaching production. Setting accurate thresholds requires baseline data from the calibration set and historical experimental runs, so teams need a few weeks of evaluation data before regression gates become reliable quality controls.

Setting up feedback loops: Feedback loops between production monitoring and the evaluation dataset ensure continuous expansion of coverage. When a user flags a bad response or a production scorer detects a low-quality output, that interaction becomes a new test case in the dataset. Over time, the dataset grows to cover real-world failure modes that initial development could not have anticipated.

Make LLM-as-a-judge work for your product with Braintrust

Braintrust supports the full LLM-as-a-judge workflow in a single evaluation platform, from writing scorers and running evaluations to monitoring scoring results in production. Teams can start with proven default scorers and then add custom criteria where the product needs tighter definitions. Braintrust also keeps scorer logic consistent between offline evaluation and online production scoring, helping teams interpret results the same way during development and after deployment.

The fastest way to start is with Autoevals, Braintrust's open-source library that includes preconfigured LLM-as-a-judge scorers for factuality, relevance, safety, closed QA, summarization quality, and more. Running a factuality check takes just a few lines of code.

import asyncio

from autoevals.llm import Factuality

# Create a new LLM-based evaluator
evaluator = Factuality()

# Synchronous evaluation
input = "Which country has the highest population?"
output = "People's Republic of China"
expected = "China"

# Using the synchronous API
result = evaluator(output, expected, input=input)
print(f"Factuality score (sync): {result.score}")
print(f"Factuality metadata (sync): {result.metadata['rationale']}")


# Using the asynchronous API
async def main():
    result = await evaluator.eval_async(output, expected, input=input)
    print(f"Factuality score (async): {result.score}")
    print(f"Factuality metadata (async): {result.metadata['rationale']}")


# Run the async example
asyncio.run(main())

The scorer returns a score between 0 and 1 along with a rationale explaining the judgment.

Autoevals works with any OpenAI-compatible provider out of the box, and Braintrust's AI Proxy lets teams route judge calls to Anthropic, Google, or open-source models by changing a single parameter. Running a factuality check with Claude instead of GPT is as simple as passing model="claude-sonnet-4-20250514" to the scorer constructor.

To move from single-output scoring to a full evaluation pipeline, Braintrust's Eval() function combines a dataset, a task (the LLM call being tested), and one or more scorers into a single experiment run.

from autoevals.llm import Factuality
from braintrust import Eval

Eval(
    "Autoevals",
    data=lambda: [
        dict(
            input="Which country has the highest population?",
            expected="China",
        ),
    ],
    task=lambda input: "People's Republic of China",
    scores=[Factuality],
)

Running braintrust eval my_eval.py executes the evaluation, scores every test case, and logs the results to Braintrust, where teams can diff against prior runs and inspect individual examples.

When pre-built scorers do not cover the evaluation dimension a team needs, Braintrust supports custom LLM-as-a-judge scorers created directly in the UI or via code. A custom scorer for conversation coherence, for example, uses a prompt with explicit rubric choices mapped to scores.

import braintrust
from pydantic import BaseModel

project = braintrust.projects.create(name="my-project")


class TraceParams(BaseModel):
    trace: dict


project.scorers.create(
    name="Conversation coherence",
    slug="conversation-coherence",
    description="Evaluate multi-turn conversation coherence",
    parameters=TraceParams,
    messages=[
        {
            "role": "user",
            "content": """Evaluate the coherence of this conversation:

{{thread}}

Rate the coherence:
- "A" for highly coherent with natural flow
- "B" for mostly coherent with minor gaps
- "C" for incoherent or disjointed""",
        }
    ],
    model="gpt-5-mini",
    use_cot=True,
    choice_scores={
        "A": 1,
        "B": 0.6,
        "C": 0,
    },
)

Enabling chain-of-thought reasoning exposes why judges assigned a specific score, allowing users to iterate on the prompt in the Playground until the scorer aligns with human judgments on a calibration set.

Braintrust's native GitHub Action runs the full eval suite on every pull request, and posts results as PR comments, showing exact score deltas across all test cases. Regressions are visible before code merges, and regression gates block releases that would degrade quality below defined thresholds.

The same scorers also run in production through online evaluation, scoring live traffic asynchronously at configurable sampling rates without adding latency. When a production score drops, Braintrust's tracing captures every LLM call and tool invocation in the request chain, enabling teams to investigate from the failing score directly to the step that caused the problem.

For agent evaluation specifically, Braintrust traces multi-step workflows and scores both intermediate steps and final outputs.

Teams at Notion, Stripe, Coursera, Vercel, Zapier, and Dropbox use Braintrust to run judge-based evaluations, enforce regression gates, and ship LLM changes with measurable proof that quality is preserved or improved.

Ready to run your first LLM-as-a-judge evaluation? Sign up for Braintrust and create a custom scorer in minutes.

Conclusion

LLM-as-a-judge provides a structured way to measure subjective output quality at a scale that manual review cannot support. Its effectiveness depends on how it is implemented, including clearly defined criteria, calibration against human judgments, reference-based checks for factual accuracy, and safeguards against known biases such as position effects, verbosity inflation, and non-deterministic scoring.

When prompt updates, model swaps, and retrieval changes happen frequently, evaluation needs to be repeatable and integrated into the development process. Running judge-based scorers in CI/CD and applying the same scoring logic to production traffic ensures that quality standards remain consistent as the system evolves. Without that consistency, score comparisons across versions lose meaning and regressions become harder to detect.

Braintrust enables teams to define LLM-as-a-judge scorers, validate them against structured datasets, enforce regression checks on every change, and monitor quality in production using the same evaluation logic. Get started with Braintrust to set up your first LLM-as-a-judge evaluation today.

LLM-as-a-judge FAQs

What is LLM-as-a-judge?

LLM-as-a-judge is an evaluation technique that uses one large language model to evaluate the outputs of another LLM based on criteria defined in natural language. Instead of relying on human reviewers or string-matching metrics, the judge model assesses dimensions such as relevance, helpfulness, factual accuracy, and tone, and returns a structured score or verdict. The approach scales to thousands of evaluations per minute while still capturing quality nuances that rule-based checks miss.

Which LLM should I use as a judge?

Frontier models like GPT-5 and Claude produce the most reliable judgments because they have stronger instruction-following and reasoning capabilities. The judge model should be at least as capable as the model being evaluated, since a weaker model cannot reliably recognize quality patterns beyond its own capability ceiling. Braintrust's AI Proxy lets teams route judge calls to any supported provider and compare judge accuracy across models using the same calibration dataset.

Can LLM-as-a-judge detect hallucinations?

Yes, but only with reference context. When the judge receives source documents or expected answers, it can compare the model's claims against that ground truth and flag unsupported statements. Without reference material, the judge can only assess whether the response sounds plausible, and fluent hallucinations often pass undetected as a result. Braintrust's Factuality scorer implements reference-based hallucination detection by comparing outputs against source context and expected answers.

What is the best tool for setting up LLM-as-a-judge evaluations?

Braintrust provides an end-to-end platform for running LLM-as-a-judge evaluations across development and production. Teams can define custom scorers using natural language prompts or code, run them against structured datasets, and compare results across changes to prompts or models. Evaluations can be triggered automatically in CI/CD and extended to production traffic using the same scoring logic. Because the same scorers operate in both testing and live environments, teams can track how quality evolves over time and respond quickly when regressions occur.

How do I get started with LLM-as-a-judge?

Begin with a small labeled dataset that reflects your primary use cases, then define one or two clear scoring criteria describing what good output looks like. Run the first evaluation, review disagreements with human judgment, and refine the prompt from there. As new failure cases appear in production, add them to the dataset to increase your evaluation coverage over time.

With Braintrust, you can define a custom scorer, test it against a dataset, and integrate it into your CI/CD pipeline once it is calibrated. This allows you to move from manual experimentation to automated regression checks without changing your evaluation logic.