In the race to build the next wave of intelligent systems, large language models (LLMs) are stepping into surprising new roles. One of the more interesting use cases is having these AI models act as “judges” to evaluate other models. It’s a concept that’s already saving teams a ton of manual labor, but questions remain: Can an LLM truly catch every subtle error? What happens when a situation calls for human intuition or deep domain expertise?
The reality is that human reviewers still offer a level of contextual understanding that AI can’t fully replicate. Rather than treat these approaches as competing solutions, many in the industry are finding that llm-as-a-judge plus human evaluation is the most effective combination. In this article, we’ll explore what exactly an LLM judge is, how it stacks up against human evaluation, and why combining these approaches makes the most sense.
What is "LLM as a judge"?
An "LLM as a judge" is an AI model that reviews and assesses outputs from other AI models. It became popular because the sheer amount of AI-generated data has grown very quickly and, at the same time, language models became smarter – smart enough to evaluate themselves. Models like GPT-4 make good judges since they provide fast, consistent, and easily repeatable assessments.
AI teams often use LLM judges as a quick initial check, highlighting obvious successes or problems before human evaluators get involved. This method simplifies and accelerates evaluations and is especially useful for tasks like automated testing, continuous integration processes, and quickly refining AI models.
Types of LLM-as-a-judge
There are several ways to use LLMs to evaluate AI outputs. You can compare two responses head-to-head, directly score a single response, or even add reference materials for extra context. Below are the main approaches and how they work.
- Pairwise comparison
In pairwise comparison, you provide an LLM with two responses and ask it to decide which one is better. This method is often used when you want to compare models, prompts, or configurations. You generate two outputs, give them to the “judge” model, and have it pick the more suitable answer based on specific criteria (such as accuracy, clarity, or tone).
- When to use it
This method can help during experimentation or model selection. You can quickly see which of two potential answers stands out, helping you pick the stronger option for further refinement.
- Example prompt
“Below are two responses to the same question. Consider their accuracy, clarity, and completeness. Indicate which response is superior, or declare a tie if they are equally good.”
- Single output scoring (reference-free)
Sometimes you only have one response to evaluate. In this scenario, you prompt the LLM to give a direct score or classification based on certain guidelines—no side-by-side comparison is involved. It can work well for ongoing monitoring or quality checks, such as ensuring content is polite, concise, or free of sensitive information.
- When to use it
It’s useful for continuous evaluation, where you want to maintain a watchful eye on how your system is performing across specific categories (e.g., tone, adherence to policy, or correctness).
- Example prompt
“Look at the following text and judge how concise it is, using the labels “Concise” or “Too Wordy.” A concise text focuses on the main idea without unnecessary extras, while a “Too Wordy” text includes superfluous details.”
- Single output scoring (reference-based)
Here, you provide both the generated answer and a reference or “ideal” response (or other context like a source document). The LLM then scores how well the generated text aligns with the reference. This can reduce variability in LLM judgments, since the reference offers a concrete example of what a “correct” answer looks like.
- When to use it
This is especially handy when you have a gold standard or official documentation to compare against. It also applies to scenarios like retrieval-augmented generation (RAG), where you want to check if the model properly leveraged the retrieved information.
- Example prompt
“You have a user’s question, a system-generated reply, and a reference answer. Please rate how closely the reply matches the reference on a scale of 1–5, where 1 indicates major discrepancies and 5 means an almost perfect match.”
Evaluating longer interactions
These methods don’t just apply to short prompts and responses. As long as the entire conversation fits into the LLM’s context window, you can assess multi-turn interactions. For instance, you could review a lengthy exchange to see if a user’s query was ultimately resolved or if the AI assistant repeated information.
LLM-as-a-judge challenges
While using an LLM to evaluate AI outputs can speed things up, it’s not a silver bullet.
- One of the biggest hurdles is ensuring the judge model aligns with your specific goals and criteria. If your instructions aren’t crystal clear—or if the LLM’s training data doesn’t cover your domain thoroughly—you can end up with unreliable or inconsistent judgments. This mismatch is especially problematic when accuracy is critical.
- Another concern is bias. Large language models sometimes inherit biases from the data they’re trained on, which can skew the evaluation. If you’re not actively monitoring and adjusting for these biases, you risk reinforcing the exact issues you were hoping to catch. On top of that, LLMs are still prone to “hallucinations,” where they might invent details or rationale that sound plausible but aren’t actually correct.
- Context also matters. An LLM has limits on how much text it can process at once, so if you’re evaluating long conversations or extensive documentation, you’ll need to manage that carefully. And let’s not forget cost—running large models for every single evaluation can add up, both financially and in terms of compute resources.
- Finally, even the best judge model still benefits from human oversight. Automated scoring is great for spotting obvious red flags and boosting throughput, but real-world nuances often require a second opinion. Striking the right balance between automated checks and human review is key.
Human-in-the-loop is crucial
Human review has been the gold standard for data quality. People can adapt on the fly to ambiguous labeling instructions, deal with corner cases, and refine the guidelines when they find something unexpected. They also help catch the mistakes an LLM might make if, for example, there’s a sudden shift in topics or the data includes unusual edge cases.
As Jensen Huang once said, “In the area of LLMs in the future of increasingly greater agency AI, clearly the answer is for as long as its sensible - and I think it's going to be sensible for a long time that is human in the loop. The ability for an AI to self-learn, impact and change out in the wild in a digital form should be avoided.
We should collect data, we should carry the data, we should train the model, we should test the model, validate the model before we release it in the wild again - so human is in the loop”
Better together: LLM-as-a-judge & human-in-the-loop
When you let an LLM do the first pass, it quickly filters out the obviously correct or incorrect annotations. That means your human reviewers don’t have to spend precious time on data that’s easy to judge. Instead, they can focus on the tricky parts. This kind of “tag team” approach often leads to better coverage and fewer errors.
Many AI teams now adopt this dual-layer approach:
- LLM for broad coverage: The model flags suspicious data points quickly, offering near-instant feedback.
- Human experts for specialized insight: Reviewers give the final word on ambiguous cases and continuously refine the model’s evaluation criteria.
Over time, the model learns from these corrections, becoming a better judge with each project iteration.
How SuperAnnotate improved Databricks’s LLM-as-a-judge
“Efficiency,” as Peter Drucker said, “is taking things that are already being done and doing them much better.” That’s precisely what SuperAnnotate achieved for Databricks.
By enhancing Databricks’ existing LLM-as-a-judge pipeline, SuperAnnotate tripled its speed and slashed costs by a factor of ten—all through a well-placed dose of human expertise.
Databricks had been working on a state-of-the-art RAG chatbot system that would be RAG-enabled, context-aware, and do tasks like retrieving complex documentation based on query, writing complex SQL code, and even debugging entire data pipeline in some cases.
LLM-as-a-judge evaluation with human enablement
However, evaluating such complex systems is non-trivial. Databrick's initial approach with GPT-3.5 produced inconsistent results, LLM-introduced bias, and subjectivity. This is why Databricks partnered with SuperAnnotate for a process that we call LLM-as-a-judge with human enablement.
Databricks partnered with SuperAnnotate to create a scalable, objective RAG eval solution, custom-tailored to their use case. They tackled this challenge in three main steps:
- Align on goals and build a framework
SuperAnnotate first helped Databricks create what “good” evaluation looked like, identify which parts of the AI process needed extra attention, and translate those insights into a clear scoring rubric. - Set up on SuperAnnotate’s platform
Next comes configuring SuperAnnotate’s platform to fit Databricks’s data and workflow. A team of experts—trained specifically for Databricks’ domain—was then onboarded to maintain consistent and accurate evaluations. - Create a golden evaluation dataset
Using the established scoring system, these experts produced a high-quality “golden dataset.” Databricks then used this dataset to retrain its LLM judge, effectively bringing GPT-3.5 performance closer to GPT-4 levels—thanks to more precise feedback and better evaluation data.
Overall, this approach allowed Databricks to reach higher performance levels with far less overhead. The success of the project also serves as a reminder that, even as we lean on AI for efficiency, human-generated data remains a fundamental piece of the puzzle. By combining targeted human oversight with automated evaluation, Databricks managed to elevate its AI capabilities while keeping costs under control.
How to create an LLM-as-a-judge
Building an effective “LLM-as-a-judge” system doesn’t have to be complicated, but there are key steps to keep in mind:
- Select a suitable model: Start with a high-performing LLM like GPT-4. If you need domain-specific performance, consider LLM fine-tuning or using specialized models.
- Clarify evaluation criteria: Specify what “correct” means. For instance, in a sentiment analysis task, define each sentiment category and provide examples. A clear set of rules reduces the model’s guesswork.
- Design prompt structures and examples: LLMs perform best when given well-crafted prompts. Include examples of both correct and incorrect labels, so the model understands what to look for.
- Set a threshold for uncertainty: Decide on a confidence score or logic that determines whether the LLM’s judgment is “certain” or “uncertain.” When the model is unsure, pass the item to a human reviewer.
- Collect feedback and iterate: Over time, gather data on where the model succeeded and where it failed. Use that information to refine prompts, update the model, or adjust labeling guidelines.
- Monitor for drift: ML models can degrade if the data distribution shifts (Stanford Center for Research on Foundation Models). Periodically check the model’s performance to ensure it hasn’t lost its touch.
By following these steps, you can tailor an LLM’s judging capabilities to your project’s needs and keep improving results through ongoing human feedback.
Final thoughts
Ultimately, “LLM-as-a-judge” isn’t meant to replace human reviewers—it’s there to take on some of the more repetitive or large-scale tasks, so experts can focus on nuanced decisions. By blending AI’s speed with human judgment, you gain the best of both worlds. You’ll catch obvious errors quickly while still relying on experienced annotators for deeper context and tricky edge cases. This balanced approach helps teams build more reliable AI systems, keeps projects on schedule, and ensures high-quality data every step of the way.
