LLM as a Judge: Guide to LLM Evaluation & Best Practices

Evaluating LLM apps is hard. Unfortunately, it's also a must.

LLMs are non-deterministic, tend to hallucinate, and are very difficult to control.

This means that if you need to build reliable LLM apps (the kind required for professional software), you need to run LLM evaluations.

The easiest way to run evaluations is to use humans. Each time you make a change to your app, you or a colleague reviews the entire test set and scores the LLM outputs. If things improved, you push to production. Problem solved.

Not so fast.

Human evaluation comes with several problems. First, it doesn't scale (and it's, to be honest, is extremely boring). If your test sets are large (which they should be), you're making frequent changes (which you need to, iteration speed is critical for successful LLM ops), or you're short on time (who isn't?), then human evaluation isn't a long-term solution. Second, it's not always consistent: reviewers may disagree on the same output. Sometimes, the same reviewer disagrees with their own assessment from the day before.

The solution: LLM-as-a-judge.

The pitch is great. Why not use LLMs to perform the evaluations themselves? We simply tell the model our requirements and let it run the evaluation faster, more consistently, and at lower cost.

In this guide, we'll explore how to design and implement an LLM-as-a-judge, the types of LLM-as-a-judge, how to prompt an LLM-as-a-judge, and the best practices to keep in mind.

Designing an LLM-as-a-Judge

In this section we will guide you through design considerations you should be taking into consideration before assigning LLMs to judge your outputs.

Define Evaluation Objectives

The first step in building an LLM-as-a-Judge system is to clearly define what you want to evaluate. Without a precise objective, even the most advanced setup will give vague or unhelpful results.

• Identify the task: Are you evaluating summarization, translation, question answering, code generation, or something else? Each task has its own success criteria.

• Choose the right metrics: Depending on the task, you might care about linguistic quality, factual accuracy, coherence, style adherence, or task-specific requirements.

• Ground the metrics in context: For instance, in summarization you might prioritize content coverage and conciseness, while in code generation, correctness and efficiency are usually the top concerns.

• Tie objectives to real-world goals: Is your evaluation aimed at research benchmarking, production monitoring, or closing the loop with user feedback? Aligning with the broader purpose ensures your evaluation remains actionable.

Here is the best process to define evaluation objectives is error analysis:

1. Sit down with a subject matter expert in the problem you are working on

2. Go over the LLM outputs and ask them about for feedback for each failed output.

3. Add each feedback, write a comment

4. Later, try to categorize and cluster the types of errors.

5. Identify the top clusters and try to create metrics for each. Is one of the issue long outputs, maybe conciseness is a good output

Choose the Evaluation Type

Once your objectives are clear, the next step is deciding how the evaluation will be conducted. In practice, there are three main approaches:

• Pointwise

  Each output is judged on its own, usually against a reference or set of criteria.

  

  Example: In summarization, an LLM might rate each summary separately for informativeness, coherence, and conciseness.

  Strengths: Simple, straightforward, and easy to scale.

  Limitations: It doesn’t capture relative quality between candidates and may introduce bias when items are evaluated in isolation.
  
  

• Pairwise

  Two outputs are compared directly, and the evaluator decides which one is better.

  

  Example: Given two summaries of the same article, the LLM is asked which is more accurate or coherent.

  Strengths: Mirrors human preference judgments and works well when differences are subtle.

  Limitations: Can become inefficient if there are many candidates to compare.
  
  

• Listwise

  Multiple outputs are ranked together as a set.

  

  Example: In a search or retrieval task, an LLM ranks all candidate documents by relevance to a query.

  Strengths: Captures interactions between candidates and provides a holistic view.

  Limitations: More complex to design, and consistency can be a challenge.

These three modes can also be connected. for instance, pointwise scores can be aggregated into pairwise comparisons or even full rankings. But in practice, LLMs don’t always behave consistently across modes (e.g., preferring A over B in one comparison but failing transitivity when adding C into the mix). These reliability issues are an important consideration, which we’ll revisit later when discussing trustworthiness in the LLM-as-a-Judge framework.

Reference Considerations

Another important design choice is whether your evaluation should be reference-based or reference-free.

• Reference-Based Evaluation

  In this setup, model outputs are compared against high-quality “gold standard” references.

  Example: In machine translation or summarization, an LLM can compare generated text with human-written references.

  Strengths: Provides a clear, objective benchmark and is widely used in tasks where good references exist.

  Limitations: The evaluation is only as good as the reference data. If references are limited or biased, the assessment may not reflect real-world quality.
  
  

• Reference-Free Evaluation

  Here, outputs are judged without relying on predefined references. Instead, evaluation focuses on intrinsic qualities such as fluency, coherence, or alignment with the source context.

  Example: In dialogue generation, an LLM might assess whether a response is natural, relevant, and contextually appropriate without needing a gold-standard reply.

  Strengths: Offers flexibility, especially for open-ended or creative tasks where references are hard to define.

  Limitations: More subjective, and accuracy can suffer in domains where the LLM lacks deep knowledge or grounding.

In practice, many evaluation pipelines mix both approaches using references when they exist, while relying on reference-free methods for broader, more open-ended tasks.

Prompt Design Tips

When building an LLM-as-a-Judge, the quality of your evaluation depends heavily on how you prompt the model. A vague or underspecified prompt can lead to inconsistent judgments, while a carefully crafted one encourages reliable scoring.

Here are some tips:

• Provide clear context

  Frame the task explicitly so the model knows what it is evaluating.

  Example: “You are evaluating summaries of news articles. Judge them for informativeness, conciseness, and coherence.”
  
  

• Define scoring criteria explicitly

  Ambiguity in scoring leads to noisy results. Spell out the dimensions and scales.

  Example: “Rate coherence on a scale of 1–5, where 1 = incoherent and 5 = logically consistent and easy to follow.”
  
  

• Include examples when possible

  Few-shot prompting helps the model align with your expectations.

  Example: Show a good vs. bad summary with the corresponding scores before asking it to evaluate new ones.
  
  

• Avoid overloaded instructions

  Keep prompts focused. If multiple dimensions matter, evaluate them separately instead of lumping them together.
  
  

• Test and iterate

  Treat prompts as part of your system design, refine them based on pilot runs and consistency checks.
  
  

• Use structured outputs

  One major difficulty when designing and LLM as a judge is output consistency. Some times the judge will return the score, while other times it would return ‘The results is {the score}`. Parsing this is challenging in an evaluation pipeline. Using structured outputs (either native structure outputs from providers or through libraries) solves this problem. You can read our post about structured outputs to learn more about all these solutions.

Designing Outputs

In the LLM-as-a-Judge paradigm, the model’s output can go beyond a simple score. Typically, it can produce three kinds of outputs:

a. Evaluation Result (Y)

• The primary output: a numeric score, ranking, categorical label, or qualitative assessment.

• Example: In machine translation, Y could be a score for translation quality; in dialogue generation, it might be a 1–5 coherence rating.

• Purpose: Provides a clear measure of performance to compare models or outputs.

b. Explanation (E)

• Optional textual reasoning behind the evaluation.

• Example: For a summary, the LLM might explain that the score was lowered due to missing key points or redundant content.

• Purpose:

  • Increases transparency

    • Helps users understand why a particular output received a given score.

  • Improves evaluation reliability

    • LLMs are probabilistic: their outputs can vary slightly even on the same input.

    • Asking the model to explain its reasoning forces it to “commit” to a chain of logic, which often reduces random or inconsistent scoring.

  • Facilitates human alignment

    • Explanations reveal whether the LLM is using criteria that humans would consider reasonable.

    • This helps detect when the model is focusing on the wrong aspects of the output.

  • Enables error diagnosis

    • If a score seems off, the explanation highlights why e.g., missing context, irrelevant content, or style errors.

    • This is invaluable for debugging model behavior or refining scoring prompts.

  • Supports iterative model improvement

    • Explanations can guide downstream training, reward modeling, or fine-tuning.

    • For instance, developers can feed reasoning examples back into a model to teach better evaluation patterns.

  • Promotes accountability and auditability

    • In high-stakes applications, explanations provide a record of how and why decisions were made, which is critical for compliance or peer review.

c. Feedback (F)

• Actionable recommendations aimed at improving the evaluated output.

• Example: In creative writing, the LLM could suggest ways to improve narrative flow or clarity.

• Purpose: Enables iterative refinement, guiding both model developers and content creators toward better outputs.

Why this matters: Combining Y, E, and F makes evaluation not just informative, but actionable. Users can trust the scores (thanks to explanations) and improve outputs (thanks to feedback), reducing the need for extensive human post-analysis.

Architectures for LLM-as-a-Judge

The architecture of an LLM-as-a-Judge system defines how the evaluation is performed and affects both reliability and scalability. Broadly, there are three main configurations:

a. Single-LLM System

• One model handles all evaluation tasks.

• Steps:

  • Step 1: Input – The system receives candidate outputs to be evaluated, such as summaries, translations, or generated code.

  • Step 2: LLM Evaluation – A single LLM judges the outputs based on the defined criteria (quality, coherence, correctness, etc.).

  • Step 3: Evaluation Output – The LLM produces one or more outputs: numeric scores, labels, explanations, or feedback.

• Pros: Simple, fast to deploy, and easy to scale.

• Cons: Limited flexibility; may inherit biases from the chosen LLM and struggle with complex or specialized tasks.

• Best for: Standard evaluations where speed and simplicity are more important than nuanced reasoning.

b. Multi-LLM System

• An ensemble of LLMs collaborates (or competes) to evaluate outputs, and results are aggregated.

• Simple averaging of scores. Weighted voting based on model confidence.

• Steps:

  • Step 1: Input Distribution – The candidate outputs are sent to multiple LLMs simultaneously. Each model may have different strengths, training data, or reasoning styles.

  • Step 2: Individual Evaluations – Each LLM independently evaluates the outputs according to the criteria.

  • Step 3: Aggregation – An aggregation layer combines the outputs from all models. This could be consensus-based methods to resolve conflicts.

  • Step 4: Final Evaluation Output – Produces a single, more robust evaluation that benefits from multiple perspectives.

• Pros: Higher reliability and broader coverage of evaluation criteria.

• Cons: More computationally expensive, harder to deploy, and requires mechanisms to reconcile differences between models.

• Best for: Complex tasks that benefit from diverse perspectives and where reliability is critical.

c. Human-in-the-Loop System

• Combines automated LLM scoring with human review for critical or high-stakes tasks.

• Steps

  • Step 1: LLM Pre-Evaluation – The LLM performs an initial assessment, producing scores, explanations, and feedback.

  • Step 2: Human Review – Humans review the LLM’s judgments, focusing on complex or critical aspects. They can:

    • Correct mistakes.

    • Adjust scores for nuanced reasoning.

    • Provide additional qualitative feedback.

  • Step 3: Final Evaluation Output – Combines automated and human insights into a trustworthy, high-quality evaluation.

• Pros: Mitigates model biases, adds nuanced judgment, and improves trustworthiness.

• Cons: Increases cost, time, and coordination complexity; less scalable than fully automated systems.

• Best for: High-stakes evaluations or cases requiring subjective, context-sensitive judgments.

Scenarios Where LLM-as-a-Judge Can Assist

LLM-as-a-Judge is not only about automation it’s about augmenting human roles across the AI lifecycle. Below are key scenarios and the roles they support:

a. Research & Benchmarking

• Role assisted: AI Researchers, Data Scientists

• Scenario: Comparing multiple LLMs on summarization or reasoning tasks.

• How it helps: Provides scalable, consistent scoring, reducing reliance on costly expert annotation.

b. Model Deployment & Monitoring

• Role assisted: MLOps Engineers, QA Specialists

• Scenario: Integrating evaluation into CI/CD to catch regressions during deployment.

• How it helps: Automates quality checks, monitors performance drift, and flags issues early.

c. Product Development

• Role assisted: Product Managers, UX Designers

• Scenario: Testing chatbot responses or content-generation features.

• How it helps: Highlights user-facing issues (e.g., coherence, tone, safety) without requiring large-scale user studies upfront.

d. Human Review & Moderation

• Role assisted: Content Moderators, Reviewers, Editors

• Scenario: Reviewing large volumes of generated text (e.g., translations, creative content, or policy compliance).

• How it helps: Surfaces strengths/weaknesses, reducing human workload and focusing attention on edge cases.

e. Training & Iterative Improvement

• Role assisted: ML Engineers, RLHF Practitioners

• Scenario: Using evaluation outputs as feedback signals in reinforcement learning or fine-tuning.

• How it helps: Provides structured signals (scores, reasoning, feedback) that improve models without full manual labeling.

Example using Agenta’s LLM-as-a-judge feature

Agenta provides LLM-as-a-Judge as a built-in evaluator, allowing you to automatically assess LLM outputs using another LLM. This is especially useful for chatbot evaluation and open-ended text generation, where correctness is subjective and style also matters.

Configure the Prompt

1. Go to the Evaluation Settings in Agenta.

2. Define the system prompt for the judge model.

   • Example criteria: check if the output matches the reference answer in meaning, assign a score between 0–10, and output only the score.

3. Define the user prompt, where you provide inputs such as:

   • country (from your test set)

   • correct_answer (optional reference answer)

   • prediction (LLM application’s output)

If no correct_answer column exists in your dataset, the variable will remain empty.

Select the Model

Choose the LLM that will act as the evaluator. Agenta supports:

• OpenAI models: gpt-3.5-turbo, gpt-4o, gpt-5, gpt-5-mini, gpt-5-nano

• Anthropic models: claude-3-5-sonnet, claude-3-5-haiku, claude-3-5-opus

🔑 You must set your OpenAI or Anthropic API key in Agenta. Keys are stored locally and only sent securely during evaluation.

Run the Evaluation

1. Upload your test set with inputs, reference answers (optional), and outputs.

2. Run the LLM-as-a-Judge evaluation.

3. The model will return numerical scores (0–10) for each evaluated row.

Review and Compare

• View aggregated metrics across multiple runs.

• Compare evaluation results for different models or prompt versions.

• Use the feedback to decide whether an application is production-ready.

Conclusion

LLMs can significantly scale evaluation, reduce costs, and provide consistent judgments across large datasets. However, they are designed to complement, not replace, human evaluators, ensuring that nuanced oversight and context are still considered. To build robust and reliable evaluation systems, engineers should focus on careful prompt design, define human-centered evaluation criteria, and engage in iterative testing. By combining automated LLM judgments with human insight, organizations can achieve efficient, accurate, and well-rounded assessments of AI outputs.