concerning the idea of using AI to judge AI, also often called “LLM-as-a-Judge,” my response was:
We live in a world where even toilet paper is marketed as “AI-powered.” I assumed this was just one other hype-driven trend in our chaotic and fast-moving AI landscape.
But once I looked into what LLM-as-a-Judge actually means, I noticed I used to be improper. Let me explain.
There may be one picture that each Data Scientist and Machine Learning Engineer should keep behind their mind, and it captures the complete spectrum of model complexity, training set size, and expected performance level:
If the duty is easy, having a small training set is generally not an issue. In some extreme cases, you may even solve it with an easy rule-based approach. Even when the duty becomes more complex, you may often reach high performance so long as you’ve got a large and diverse training set.
The actual trouble begins when the duty is complex and also you don’t have access to a comprehensive training set. At that time, there isn’t any clean recipe. You would like domain experts, manual data collection, and careful evaluation procedures, and within the worst situations, you may face months and even years of labor just to construct reliable labels.
… this was before Large Language Models (LLMs).
The LLM-as-a-Judge paradigm
The promise of LLMs is easy: you get something near “PhD-level” expertise in lots of fields that you could reach through a single API call. We are able to (and doubtless should) argue about how “intelligent” these systems really are. There may be growing evidence that an LLM behaves more like an especially powerful pattern matcher and knowledge retriever than a really intelligent agent [you should absolutely watch this].
Nonetheless, one thing is difficult to disclaim. When the duty is complex, difficult to formalize, and you don’t have a ready-made dataset, LLMs could be incredibly useful. In these situations, they provide you with high-level reasoning and domain knowledge on demand, long before you could possibly ever collect and label enough data to coach a standard model.
So let’s return to our “” red square. Imagine you’ve got a difficult problem and only a really rough first version of a model. Perhaps it was trained on a tiny dataset, or possibly it’s a pre-existing model that you’ve got not fine-tuned in any respect (e.g. BERT or whatever other embedding model).
In situations like this, you should utilize an LLM to judge how this V0 model is performing. The LLM becomes the evaluator (or the judge) on your early prototype, providing you with immediate feedback without requiring a big labeled dataset or the massive effort we mentioned earlier.
This might have many useful downstream applications:
- Evaluating the state of the V0 and its performance
- Constructing a training set to enhance the prevailing model
- Monitoring the stage of the prevailing model or the fine-tuned version (following point 2).
So let’s construct this!
LLM-as-a-Judge in Production
Now there’s a fake syllogism: as you don’t must train an LLM they usually are intuitive to make use of on the ChatGPT/Anthropic/Gemini UI, then it should be easy to construct an LLM system. That’s the case.
In case your goal will not be an easy plug-and-play feature, you then need energetic effort to be certain your LLM is reliable, precise, and as hallucination-free as possible, designing it to fail gracefully when it fails (not but ).
Listed below are the predominant topics we are going to cover to construct a production-ready LLM-as-a-Judge system.
- System design
We are going to define the role of the LLM, the way it should behave, and what perspective or “persona” it should use during evaluation. - Few-shot examples
We are going to give the LLM concrete examples that show exactly how the evaluation should look for various test cases. - Triggering Chain-of-Thought
We are going to ask the LLM to supply notes, intermediate reasoning, and a confidence level as a way to trigger a more reliable type of Chain-of-Thought. This encourages the model to really “think.” - Batch evaluation
To cut back cost and latency, we are going to send multiple inputs directly and reuse the identical prompt across a batch of examples. - Output formatting
We are going to use Pydantic to implement a structured output schema and supply that schema on to the LLM, which makes integration cleaner and production-safe.
Let’s dive within the code! 🚀
Code
The entire code could be present in the next GitHub page [here]. I’m going to undergo the predominant parts of it in the next paragraph.
1. Setup
Let’s start with some housekeeping.
The dirty work of the code is completed using OpenAI and wrapped using llm_judge. For that reason, the whole lot it’s good to import is the next block:
Note: You will have the OpenAI API key.
All of the production-level code is handled on the backend (thank me later). Let’s carry on.
2. Our Use Case
Let’s say we’ve got a sentiment classification model that we wish to judge. The model takes customer reviews and predicts: Positive, Negative, or Neutral.
Here’s sample data our model classified:
For every prediction, we wish to know:
– Is that this output correct?
– How confident are we in that judgment?
– Why is it correct or incorrect?
– How would we rating the standard?
That is where LLM-as-a-Judge is available in. Notice that ground_truth is definitely not in our real-world dataset; for this reason we’re using LLM in the primary place. 🙃
The one reason you see it here is to display the classifications where our original model is underperforming (index 2 and index 3)
Note that on this case, we’re to have a weaker model in place with some errors. In an actual case scenario, this happens once you use a small model otherwise you adapt a non fine-tuned deep learning model.
3. Role Definition
Similar to with any prompt engineering, we’d like to obviously define:
1. Who’s the judge? The LLM will act like one, so we’d like to define their expertise and background
2. What are they evaluating? The particular task we wish the LLM to judge.
3. What criteria should they use? What the LLM has to do to find out if an output is sweet or bad.
That is how we’re defining this:
Some recipe notes: Use clear indications. Provide what you wish the LLM to do (not what you wish it to do). Be very specific within the evaluation procedure.
4. ReAct Paradigm
The ReAct pattern (Reasoning + Acting) is built into our framework. Each judgment includes:
1. Rating (0-100): Quantitative quality assessment
2. Verdict: Binary or categorical judgment
3. Confidence: How certain the judge is
4. Reasoning: Chain-of-thought explanation
5. Notes: Additional observations
This permits:
– Transparency: You possibly can see why the judge made each decision
– Debugging: Discover patterns in errors
– Human-in-the-loop: Route low-confidence judgments to humans
– Quality control: Track judge performance over time
5. Few-shot examples
Now, let’s provide some more examples to be certain the LLM has some context on learn how to evaluate real-world cases:
We are going to put these examples with the prompt so the LLM will learn learn how to perform the duty based on the examples we give.
Some recipe notes: Cover different scenarios: correct, incorrect, and partially correct. Show rating calibration (100 for perfect, 20-30 for clear errors, 60 for debatable cases). Explain the reasoning intimately. Reference specific words/phrases from the input
6. LLM Judge Definition
The entire thing is packaged in the next block of code:
Similar to that. 10 lines of code. Let’s use this:
7. Let’s run!
That is learn how to run the entire LLM Judge API call:
So we are able to immediately see that the LLM Judge is appropriately judging the performance of the “model” in place. Particularly, it’s identifying that the last two model outputs are incorrect, which is what we expected.
While this is sweet to indicate that the whole lot is working, in a production environment, we are able to’t just “print” the output within the console: we’d like to store it and be certain the format is standardized. That is how we do it:
And that is the way it looks.
Note that we’re also “batching”, meaning we’re sending multiple pieces of input directly. This protects cost and time.
8. Bonus
Now, here is the kicker. Say you’ve got a totally different task to judge. Say you desire to evaluate the chatbot response of your model. The whole code could be refactored using a couple of lines:
As two different “judges” change only based on the prompts we offer the LLM with, the modifications between two different evaluations are extremely straightforward.
Conclusions
LLM-as-a-Judge is a straightforward idea with a whole lot of practical power. When your model is rough, your task is complex, and also you don’t have a labeled dataset, an LLM can enable you evaluate outputs, understand mistakes, and iterate faster.
Here’s what we built:
- A transparent role and persona for the judge
- Few-shot examples to guide its behavior
- Chain-of-Thought reasoning for transparency
- Batch evaluation to save lots of time and value
- Structured output with Pydantic for production use
The result is a versatile evaluation engine that could be reused across tasks with only minor changes. It will not be a alternative for human evaluation, nevertheless it provides a robust place to begin long before you may collect the vital data.
Before you head out
Thanks again on your time. It means loads ❤️
My name is Piero Paialunga, and I’m this guy here:
I’m originally from Italy, hold a Ph.D. from the University of Cincinnati, and work as a Data Scientist at The Trade Desk in Latest York City. I write about AI, Machine Learning, and the evolving role of knowledge scientists each here on TDS and on LinkedIn. When you liked the article and need to know more about machine learning and follow my studies, you may:
A. Follow me on Linkedin, where I publish all my stories
B. Follow me on GitHub, where you may see all my code
C. For questions, you may send me an email at
