LLM as a Judge: Practical Example with Open-Source MLRun

LLMs can be used for evaluating other models, which is a method known as “LLM as a Judge”. This approach leverages the unique capabilities of LLMs to assess and monitor the performance and accuracy of models. In this blog, we will show a practical example of operationalizing and de-risking an LLM as a Judge in with the open-source MLRun platform.

“LLM as a judge” refers to using LLMs to evaluate the performance and output of AI models. The LLM can analyze the results based on predefined metrics such as accuracy, relevance, or efficiency. It may also be used to compare the quality of generated content, analyze how models handle specific tasks, or provide insights into strengths and weaknesses.

LLM as a Judge is an evaluation approach that helps bring applications to production and derives value from them much faster. This is because LLM as a Judge allows for:

• Availability – LLMs operate 24/7, providing instant feedback in time-sensitive contexts.
• Adaptability – Prompt engineering allows easily adjusting evaluation criteria.

When using a Large Language Model (LLM) as a judge for evaluating other models, several significant risks must be carefully considered to avoid faulty conclusions:

• Bias propagation – LLMs are trained on vast datasets that may contain inherent biases related to race, gender, or culture. If these biases are not addressed, they can directly affect the evaluation process, leading to unjust or skewed assessments of the models being tested.
• Over-reliance on language and syntax – The LLM may favor models that produce more fluent or persuasive language over those that generate more accurate or innovative content. This creates the risk of misleading results.
• Hallucinations – When the LLM generates plausible-sounding but incorrect or irrelevant information. This becomes problematic during model evaluation as the LLM might misinterpret the data or generate false positives/negatives in its assessment.
• Ground truth or benchmarking – The LLM might inaccurately assess models in specialized fields like law, medicine, or science. Without access to verifiable facts or empirical data, the LLM may rely too heavily on its own internal reasoning processes, which can be flawed, resulting in unreliable judgments.
• Model drift -Updates to the LLM or changes in its underlying data can shift its evaluation standards over time, leading to inconsistency in assessments.
• Model Updates – When using third-party LLMs, updates to the model might modify performance, even breaking it.

Addressing these risks requires thorough validation, human oversight, careful design of evaluation criteria and evaluating the model Judge for the task. This will ensure reliable and fair outcomes when using an LLM as an evaluator.

In this example, we’ll show how to implement LLM as a Judge as part of your monitoring system with MLRun. You can view the full steps with code examples here.

Here’s how it works:

1. Create a LLM as a Judge monitoring application (or use the one shown in the demo).
2. Set it in the MLRun project as a monitor application.
3. Deploy it and enjoy.

To prompt engineer the judge you can follow the best practices here:

1. Create an evaluation set the judge can be scored on.
2. Build a prompt with multiple explanations about the metric, scores and add multiple examples the LLM can learn from.
3. Try it out with a few examples.
4. Run the evaluation set and check the performance.
5. Do it periodically to ensure the judge is on track.

Why is using an LLM as a Judge beneficial for bringing applications to production?

LLM as a Judge allows teams to evaluate outputs automatically, at scale, instead of relying solely on manual human review. This continuous testing of responses accelerates the transition from prototyping to production. It also reduces costs by filtering poor outputs early, so developers only spend time fine-tuning models that meet baseline performance standards.

How can the risks of using an LLM as a Judge be mitigated?

One of the biggest risks is bias or inconsistency in the “judge” model itself. To mitigate this, teams should use human-in-the loop, prompt engineering and regular monitoring of the judge. This helps ensure the Judge remains reliable over time.

What are the primary challenges when implementing an LLM as a Judge?

1) Different LLMs may rate the same output differently, making reproducibility difficult. 2) LLM judgments can lack transparency, so it’s not always clear why a certain score was assigned, complicating debugging. 3) Running an LLM-as-a-Judge across large datasets or real-time applications requires significant compute resources. 4) Aligning the judge’s criteria with business metrics.

How can I implement an LLM evaluation framework in my projects?

Define clear evaluation criteria, create evaluation prompts for the judge model that instruct it to assess outputs based on those criteria, and implement evaluation pipelines where candidate outputs are scored, logged, and compared to historical benchmarks. Tools like MLRun or custom dashboards can help manage experiment tracking, metrics aggregation, and governance.

What metrics are used in LLM evaluations?

LLM evaluation metrics vary by use case but typically include accuracy (correctness of outputs), relevance (how well the response addresses the query), fluency (clarity and coherence of text), and safety (absence of harmful or policy-violating content). More advanced systems also measure factual consistency (alignment with ground truth), helpfulness (practical utility to the user), and bias/fairness indicators. In generative scenarios, metrics like diversity, creativity, or engagement may be tracked.

LLM as a Judge is a useful method that can scale model evaluation. With MLRun, you can quickly fine-tune and deploy the LLM that will be used as a Judge, so you can operationalize and de-risk your gen AI applications. Follow this demo to see how.

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