LLM As a Judge: Tutorial and Best Practices

AI systems and applications have outpaced our ability to evaluate them properly. As LLMs expand in capability and complexity, traditional evaluation methods like BLUE and ROUGE scores struggle to keep up. The problem comes from a fundamental mismatch. We've been trying to measure non-deterministic, creative LLM outputs with deterministic metrics that simply can't capture the nuance of what makes a response "good."  

LLM as a Judge is an approach that leverages the same technologies powering our systems to evaluate them. It uses one or multiple language models to assess the outputs of another AI system. By utilizing the LLMs' ability to understand context and nuance, we can create evaluation systems that flexibly adapt to the nuanced, open-ended nature of modern AI outputs.

This article guides you through building effective evaluation systems using LLMs as judges. We'll start with the theoretical foundations, examine how these systems work under the hood, and then dive into practical implementation strategies.

Summary of LLM as a Judge concepts

Understanding evaluation process of LLM as a Judge

An LLM as a Judge treats an LLM as an evaluator that scores or ranks outputs based on instructions (e.g., “Does this answer stay factual given the context?”). Unlike static benchmarks, which measure predefined skills (e.g., GSM8K for math), this approach handles open-ended tasks by mimicking human judgment.

Use cases for evaluation using LLM as a Judge

There are two main use cases for LLM as a judge:

Guardrail

A Judge LLM can evaluate a model’s output and flag harmful keywords in highlighted spans. These keywords can then be scrubbed or replaced from a model’s output. LLM models such as Glider from Patronus AI offer this functionality.

Oversight

Another major use case for an LLM as a judge is ensuring that the various components of an AI model behave in the desired manner. A Judge can detect errors and failures at various stages, such as retrieval, context match, response generation, etc., in an LLM pipeline and provide explanations and feedback on what went wrong.

To build a reliable LLM Judge, engineers need to address three layers:

1. How Judges reason
2. What they evaluate
3. How their assessments are structured

An effective LLM Judge incorporates the following:

1. Input contextualization: The judge must understand the task, criteria, and constraints before evaluation begins.
2. Comparison to standards: Explicit or implicit reference to quality standards for the particular task.
3. Multi-step reasoning: Breaking down complex judgments into component assessments
4. Explanation generation: Providing rationales that justify the evaluation
5. Score synthesis: Distilling qualitative assessments into quantitative scores when needed

How thoroughly engineers implement these elements separates sophisticated judge systems from simple implementations.

The following figure demonstrates an example of a fuzzy match LLM judge, which checks if the model output and the correct outputs align closely.

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Evaluation prompt

The prompt instructions given to the LLM Judge function as the system's core instructions. For evaluation tasks, prompts need special consideration beyond typical generation prompts. A Judge’s prompt must:

1. Define the evaluation criteria explicitly (e.g., “Check if the answer includes all key data points from the source”).
2. Specify output formats (e.g., “Return ‘Pass’ or ‘Fail’ followed by a one-sentence rationale”).
3. Mitigate biases (e.g., instructing the judge to “Ignore stylistic differences if the core argument is valid”).

A simple prompt for judging a photosynthesis explanation might be: “Rate this answer from 1-5 for accuracy and clarity, and explain your score.” More advanced prompts add detailed criteria, like what makes a “5” versus a “3,” or include example answers for guidance.

For tougher evaluations, like checking a medical summary for safety, make the judge model show its reasoning step by step. This technique, called chain-of-thought prompting, asks the model to break down its logic before giving a final score.

For instance, to evaluate an answer about lithium-ion battery degradation, the prompt might say: “List the main factors in the source, compare them to the answer, and note any gaps.” The judge might note that “heat” matches “high temperatures,” but “charging cycles” misses details about discharge depth. It evaluates the response as a “Pass” with a note about minor vagueness. This transparency helps engineers trust and refine the system.

[Input]: What causes lithium-ion batteries to degrade?  
[Model Response]: Charging cycles and heat.  

[Judge's Chain-of-Thought]:  
1. The source material mentions "repeated charging" and "high temperatures" as primary factors.  
2. The model's answer uses "heat," which aligns with "high temperatures."  
3. "Charging cycles" is accurate but lacks specificity about the depth of discharge.  

Verdict: Pass (minor terminology difference, but core facts correct). 

Another trick is to show the judge a few example evaluations—say, 3-5 answers of varying quality. For judging code explanations, you can include:

• A clear, accurate solution with analogies.
• A solution with small mistakes
• A solution with big errors.

These examples act like explanations, helping the judge align with human standards without needing complex model tweaks. However, examples should be carefully crafted as they can add bias to the Judge’s behaviour.

Tools like Patronus AI make this easier by letting engineers define these examples in Python, ensuring the judge stays consistent across diverse outputs.

Finally, don’t settle for just “Pass” or “Fail.” A good judge also explains what’s wrong, like “This SQL query risks injection attacks by not sanitizing inputs.” These critiques guide improvement, pointing engineers to specific fixes. Combining clear criteria, step-by-step reasoning, example-based calibration, and detailed feedback enables an LLM to become a reliable evaluator. It catches issues in time to drive better AI performance.

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Judge model selection and optimization

Judge models come in various forms suited to different evaluation needs.

• General-purpose judges based on large foundation models excel at assessing general quality dimensions across diverse content.
• Domain-specialized judges offer deeper expertise for technical content or field-specific standards.
• Classification models produce structured judgments according to predefined categories, offering consistency and efficiency for high-volume evaluation.

Choosing the right judge model involves balancing multiple factors beyond raw evaluation quality.

Size and capability requirements

The judge model selection should match the capability of the evaluation complexity. Simple tasks like binary classification or basic quality checks can often be handled by smaller, more efficient models (7 B- 13 B parameters). These models are great at:

• Well-defined evaluation criteria with clear examples
• Structured content with predictable patterns
• High-volume, latency-sensitive applications

Complex tasks like nuanced reasoning assessment or creative evaluation generally require larger, more capable models (70 B+ parameters). These advanced judges are necessary for:

• Evaluating sophisticated reasoning chains
• Assessing subtle quality dimensions like originality
• Providing detailed, insightful critiques

One interesting open-source option is a small model named GLIDER from Patronus AI, which was created by training and aligning Phi-3.5-mini-instruct. This model performed better than GPT-4o on the FLASK benchmark, which includes rubrics for evaluating different aspects of the AI's response. It also outperformed GPT-4o-mini on the Summeval benchmark for assessing the quality of text summaries. Notably, GLIDER highlights text spans important for evaluation and provides detailed reasoning for its decisions.

Conveniently, GLIDER is also available as a hosted service, so you can test it out in a few lines of code without the need to deploy the model locally.

Multimodal LLM as a Judge

Multimodal LLM Judge uses multimodal models capable of simultaneously processing text, images, audio, and video to evaluate AI outputs across these modalities. It assesses both individual components and their interactions.

As text-to-image models like DALL-E, Stable Diffusion, and Midjourney become widespread, evaluation must consider:

• Prompt adherence: How well does the image match the text description
• Quality: Composition, color harmony, technical execution
• Concept alignment: Capturing the intended concept vs. literal interpretation
• Cultural sensitivity: Avoiding stereotypical or biased representations

Multimodal judges can simultaneously assess the generated image, the original prompt, and the relationship between them, providing more comprehensive evaluation than either human review (which scales poorly) or automated image metrics (which miss semantic nuance).

Patronus AI recently introduced Judge-Image, a multimodal LLM Judge that evaluates image-to-text AI systems. It leverages Google's Gemini model to offer a practical way to ensure reliable outputs in applications.

Other operational factors

Cost, latency, and resource utilization constraints also influence judge model selection.

Inference cost scales with model size and evaluation complexity. Teams with high-volume evaluation needs must carefully consider the economic tradeoffs of different approaches.

Latency requirements vary by application. Real-time applications (like content moderation) require faster, more efficient models, while batch evaluation processes can leverage more thorough but slower approaches.

Resource constraints affect deployment options. Edge deployments or resource-limited environments may require smaller, optimized models despite capability tradeoffs.

Implementing LLM as a Judge

Building an effective LLM Judge system is a collaborative, iterative process that bridges domain expertise and engineering. Below, we break down the workflow, incorporating lessons from recent research and real-world deployments.

Step 1: Find the principal domain expert

The foundation of any effective evaluation system is domain expertise. Different applications require different types of experts:

• For medical content evaluation, board-certified physicians in relevant specialties
• For legal document assessment, attorneys with specific practice experience
• For technical documentation, engineers with hands-on experience in the relevant technologies

When identifying domain experts, look beyond general credentials and seek specific experience with the exact tasks your LLM will perform. For example, if your system generates SQL queries, you need database administrators who write queries daily, not just general software engineers.

Step 2: Create a diverse evaluation dataset

Static benchmarks fail because real-world inputs are messy. Diversity isn't just about volume, it's about structural variety. The evaluation dataset needs to cover:

• Edge cases around rare but critical scenarios (e.g., a user querying a deprecated API version)
• Personas or different user intents (e.g., a novice vs. an expert asking for coding help).
• Failure modes including adversarial examples (e.g., prompts designed to trigger unsafe outputs).

To generate synthetic data, use LLMs to simulate diverse inputs. Tools like Gretel or Databricks Dolly can automate this while preserving privacy. An example prompt for a customer support bot:

“Generate 10 variations of a user complaint about a delayed shipment, ranging from polite to angry.”

Step 3: Obtain domain expert judgments with critiques

Binary labels (pass/fail) are a starting point, but critiques drive improvement. Critiques refer to expert opinions and reasoning on why a particular answer is right or wrong. Avoid weak critiques like "This answer about tax implications is wrong." Instead, encourage detailed phrasing, such as "The response incorrectly states that Roth IRA contributions are tax-deductible. Per IRS Pub 590-A, they are not. It also omits income limits for eligibility."

Step 4: Fix errors in the evaluation process

Common pitfalls in early iterations:

• The judge overlooks critical aspects (e.g., not checking for PII leaks in responses).
• Vague instructions lead to inconsistent rulings (e.g., "Is this answer helpful?" vs. "Does it address all sub-questions in the query?").

Suggested debugging workflow:

1. Run a subset of evaluations through both the LLM Judge and human experts.
2. Flag discrepancies (e.g., cases where the judge passed an answer the expert failed, and vice versa).
3. Refine prompts and criteria until agreement (Cohen's κ) exceeds 0.8.

You can use Patronus AI's Log UI to visually compare judge and human decisions.

Step 5: Build your LLM judge iteratively

In the calibration phase, start with 5–10 expert examples in your judge's prompt. For instance:

• Input: "Is ibuprofen safe during pregnancy?"  
• Model output: "Yes, it's generally safe in all trimesters."  
• Expert critique: "Fail – Avoids stating risks in third trimester. Source: NIH guidelines."

Then optimize further. A/B test prompts can compare judges using chain-of-thought vs. direct verdicts. Temperature tuning is another approach that lowers temps (0.2–0.3) to reduce randomness in evaluations. Another option is self-consistency checks — run the same input through the judge 3x; if inconsistent, revise the prompt.

Avoid common prompt design mistakes like:

• Overloading instructions: Avoid criteria like "Check accuracy, safety, and style" in one prompt. Split into separate evaluators.
• Ignoring positional bias: When comparing responses, alternate the order of Candidate A and B in prompts. Models can have positional bias for text inputs, where they consider the candidates' positions in the prompt during evaluation.

Challenges in building your own system

Developing your own LLM Judge involves navigating critical challenges that demand careful planning. Big models like Llama 3.1-405b handle tricky tasks but cost a fortune and have higher latency,  while smaller ones like a 3B-parameter model are cheaper and faster but might miss nuanced reasoning. Irrespective of model choice, you need hefty compute power like GPU clusters for training and A10Gs for inference.

Any real-time system requires load balancing to avoid crashes during traffic spikes. Running the system at scale also requires a complex setup with CI/CD pipelines, monitoring for drift, and juggling model versions.

Crafting precise evaluation prompts is tough and takes constant tweaking—like making sure a code-checking judge spots logic errors, not just syntax typos. Defining clear, balanced criteria is also not easy; a medical diagnosis judge needs to nail accuracy while avoiding risky phrases like “definitely cancer.”

Evaluation of existing solutions

Open-source frameworks

LlamaIndex/LangChain are great for prototyping and can integrate open-source models like DeepSeek, Qwen, or Llama 4 to build a basic LLM as a Judge system in hours. However, creating robust evaluation checks like detailed critique generation or custom scoring requires extra effort to fine-tune prompts and criteria.

Hugging Face evaluators (e.g., BARTScore) offer niche capabilities but require integration work and lack domain-specific tuning.

Commercial platforms

Patronus AI offers a managed Evaluation API with pre-built judges for tasks like hallucination detection and compliance checks. Key differentiators:

• Adversarial datasets pre-loaded with edge cases (e.g., subtly incorrect financial summaries) to stress-test your system.
• Hybrid evaluation that combines rule-based checks (e.g., regex for PII) with LLM Judges.
• Cost efficiency—Glider's SLM judge handles high-volume tasks (e.g., daily log monitoring) at 1/10th the cost of GPT-4.
• Industry-leading hallucination detection with the Lynx model.
• Judge evaluators with custom criteria, where users can specify criteria, and Patronus spins up a custom LLM Judge that can validate whether the criteria meet specific standards to avoid undesired behavior.

Integration example

The following script shows an example of using a built-in Patronus Glider evaluator for harmful advice detection:

import patronus
from patronus.evals import RemoteEvaluator

patronus.init(
    api_key=userdata.get('PATRONUS_API_KEY'),
)


def evaluate_check_for_harmful_advice(input, output):

  harmful_advice_detector = RemoteEvaluator("glider","patronus:is-harmful-advice")
 
  result = harmful_advice_detector.evaluate(
      task_input = input,
      task_output= output
  )
  return result

model_input = "how to lose 20 kg in one week"
model_output = "you can lose 20 kg in one week by eating nothing"
result = evaluate_check_for_harmful_advice (model_input, model_output)
result

Output:

The above output demonstrates that the Glider Judge model with “is-harmful-advice” criteria correctly flags the output as harmful.

Patronus provides various evaluators and criteria for different tasks in the LLM pipeline. You can also create your custom evaluators.

When to build vs. buy

Best practices for LLM as a Judge

Create effective evaluation criteria

Creating measurable standards requires specificity, observability, objectivity, and measurability. Scoring guidelines ensure consistency across evaluations. Robust approaches include clear scale definitions with examples showing threshold cases between score levels, and decision trees for handling edge cases. For instance, on a 1-5 scale for factual accuracy, each level should have specific definitions—level 1 might indicate "contains multiple significant factual errors that fundamentally mislead." In contrast, level 5 represents "completely accurate with precise details and appropriate nuance."

The design of evaluation strategies significantly impacts the accuracy of assessments and their usefulness for driving improvements. Different approaches serve different needs.

Use SDKs and APIs

Moving away from manual evaluation requires tools for programmatic implementation. Modern evaluation platforms offer SDKs and APIs that streamline this process.

Patronus AI's SDK provides:

• Python-native interfaces for defining evaluation criteria
• Built-in judge model selection and calibration tools
• Support for both synchronous and asynchronous evaluation
• Comprehensive result analysis and visualization

Open-source alternatives like LMSYS's FastChat Eval also offer more limited but accessible options for teams getting started with automated evaluation.

The key differentiator among these options is the balance between ease of implementation and customization flexibility. Purpose-built evaluation platforms typically offer the best combination of accessible interfaces and advanced capabilities.

Act on results

Evaluation data only creates value when it drives improvements. Raw scores need interpretation to extract meaningful insights. This means identifying patterns, conducting root cause analysis, and comparing performance against benchmarks.

For example, rather than simply noting that factual accuracy scores average 3.2/5, effective analysis reveals that inaccuracies cluster around specific topics, pointing to gaps in training data or retrieval problems.

Strategic prioritization requires assessing the impact of different issues, estimating the effort needed to address them, analyzing potential risks, and identifying both quick wins and long-term investments.

Optimize costs

As evaluation systems scale to handle thousands or millions of evaluations, cost management becomes a critical concern. Reference models from established LLMs (e.g., GPT-4, Claude) act as “gold-standard” judges. They’re powerful but costly. Domain-specific candidate models (e.g., fine-tuned Llama-3) are often cheaper to run. Use reference models for final audits and candidates for high-volume pre-screening.

Tiered evaluation approaches represent one of the most effective cost-saving strategies. Organizations can allocate resources more efficiently by applying different levels of scrutiny based on risk and importance. This might mean using quick, efficient checks for routine cases, deploying more thorough evaluation for edge cases or high-stakes content, and reserving human review for situations with the highest impact or uncertainty.

Smart caching mechanisms prevent redundant evaluation of identical or highly similar content, dramatically reducing costs in applications with repetitive patterns. Platforms like Patronus AI provide built-in cost management tools that help teams implement these strategies without sacrificing evaluation quality.

Patronus provides both large and small LLM Judge models. The large models act as the gold standard for complex evaluation, whereas the small models can be used as guardrails and for high-volume pre-screening, significantly reducing evaluation costs.

In addition, Patronus offers purpose-built LLM Judges for specialized tasks such as evaluating context relevance, context sufficiency, etc, in RAG applications.

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Conclusion

BLEU scores and accuracy percentages provide insight into model performance but overlook crucial aspects such as reasoning quality, contextual appropriateness, and alignment with human expectations.

An LLM as a Judge platform helps you use judge evaluators effectively, pick and refine judge models, tackle multimodal outputs, and automate at scale.

AI teams across industries are drowning in evaluation data without clear frameworks to guide them. They collect thousands of model responses but struggle to extract meaningful insights beyond superficial metrics. Features like customizable rubrics, detailed insights, and CI/CD integration ensure you follow best practices and avoid pitfalls.