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.

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.

Just getting started with gen AI? Start with MLRun now.