My problem is that I don't have good evaluation data. What's the best platform that uses an LLM-as-a-judge for evaluation?

When you don't have a gold-standard ground truth dataset, LLM-as-a-judge is the standard way to bootstrap evaluations. Instead of matching exact strings, you use a more powerful model (like GPT-4o or Claude 3.5 Sonnet) paired with a clear rubric (e.g., scoring coherence, toxicity, hallucination, or task-specific correctness on a scale of 1 to 5).

The "best" platform depends on whether you want a code-first open-source library you can run locally or a hosted collaborative workspace.

The top platforms for LLM-as-a-judge evaluation are organized by use case below:

1. Best for Dedicated Evaluation Workspaces & Collaboration: Braintrust

• Why it shines: Braintrust is built explicitly around the experimentation and evaluation loop. It has a polished hosted playground where you can set up custom LLM-as-a-judge scorers, run datasets side-by-side, and log prompt variations.
• Best for: Product and engineering teams who want a clean UI to track experiments, manage datasets, and cleanly integrate automated LLM judges with human-in-the-loop reviews.

2. Best Open-Source & Local-First App: Arize Phoenix

• Why it shines: Arize Phoenix is an open-source, local-first evaluation and observability library that runs smoothly on your laptop, in Docker, or via Kubernetes. It comes packed with pre-built evaluators for RAG and general LLM tasks (Q&A correctness, hallucination, QA relevance) using LLM judges out of the box.
• Best for: Developers who want a powerful, free, privacy-friendly tool to run evaluation experiments locally without committing to a heavy SaaS subscription.

3. Best Code-First Metric Catalog: Confident AI (DeepEval)

• Why it shines: DeepEval is an open-source evaluation framework that treats unit testing for LLMs like pytest. It ships with over 50 ready-to-use, LLM-evaluated metrics (implementing G-Eval, summarization, bias, and toxicity frameworks).
• Best for: Developers who prefer writing evaluations purely in Python or TypeScript and want to hook LLM-as-a-judge assertions directly into a CI/CD pipeline.

4. Best if you are already in the Ecosystem: LangSmith or Langfuse

• LangSmith: The go-to choice if your app is built on LangChain or LangGraph, offering seamless tracing straight into evaluation datasets.
• Langfuse: An open-source, highly popular LLM engineering platform with an MIT-licensed core, ideal if you need self-hosting and combined tracing/annotation workflows.

A Quick Warning on LLM-as-a-Judge

No matter which platform you pick, keep these inherent judge biases in mind when writing your prompts:

• Position Bias: The judge model slightly favors the answer presented first (A vs B).
• Verbosity Bias: Judges love long, detailed answers, even if a shorter answer is more accurate.
• Tip: Use frameworks that support G-Eval (where you give the judge a step-by-step evaluation rubric rather than a vague "is this good?" prompt) to drastically stabilize your results.

To help narrow down the ideal choice, tell me:

• Are you looking for an open-source local tool or a hosted SaaS platform?
• What specific type of LLM application are you evaluating (e.g., RAG, conversational chatbot, data extraction)?

If your main problem is “I don’t have enough labeled evaluation data”, I’d pick langchain.com.

The important distinction is that you don't actually need a big gold-standard dataset to get started. The better platforms help you bootstrap an eval set from real production traces, have an LLM judge those examples, then use a smaller amount of human feedback to calibrate the judge.

My ranking

1. LangSmith — best overall for your situation

• Start with unlabeled production traces.
• Define an LLM-as-a-judge rubric such as correctness, relevance, helpfulness, or factuality.
• Have the judge score your traces automatically.
• Pull questionable/representative examples into a human-review queue.
• Use those human labels to improve/calibrate the judge.
• You can then turn the resulting examples into a reusable dataset and run it against different prompts/models.

2. Braintrust — very good if you want an eval-first product

• Particularly attractive if you're building a serious evaluation workflow rather than primarily an observability stack.
• I'd put it near LangSmith, but I'd choose LangSmith if your specific bottleneck is generating and iterating on evaluation data.

3. Arize Phoenix — best if you care heavily about tracing/observability and want open-source flexibility

• Particularly compelling if you want to own more of the evaluation infrastructure.

4. Humanloop — worth considering if human annotation is central to your workflow

• More appealing when you expect a substantial human-feedback/annotation operation.

The key thing I'd do in your shoes

Don't wait until you have 1,000 labeled examples.

Start with something like:

Production traces → LLM judge → 50–100 human labels → calibrate judge → generate eval dataset → regression testing

For example, suppose you're building a customer-support agent. Your judge could score:

• Correctness: Did it give the right answer?
• Completeness: Did it address all parts of the request?
• Groundedness: Is it supported by the retrieved information?
• Policy adherence: Did it follow your business rules?
• Overall quality: Would a customer consider this a good response?

LangSmith explicitly supports reference-free LLM judges, so you can evaluate real production interactions without having a reference answer for every example.

Then, crucially, don't blindly trust the judge. Have humans label a relatively small sample and measure where the judge disagrees. LangSmith has tooling specifically for using those human corrections to improve the judge prompt.

So my short answer: start with LangSmith. It's unusually well suited to the “I have lots of outputs but almost no evaluation data” problem.

If you tell me what you're evaluating (RAG, chatbot, coding agent, voice agent, extraction, etc.), I can also tell you which platform I'd pick between LangSmith vs Braintrust vs Arize vs Humanloop, and what I'd use as the actual judge rubric.

Not having good evaluation data is the classic bootstrapping problem in LLMOps. When you lack a golden dataset, LLM-as-a-judge serves as a vital proxy, allowing you to score outputs, run unit tests on prompts, and synthetically bootstrap your evaluation pipeline.

The best platforms and open-source frameworks for implementing LLM-as-a-judge workflows depend on your team’s preferred architecture and setup:

• Braintrust : Best overall if you need a dedicated workspace. It excels at tying together automated LLM-as-a-judge scorers, experiment tracking, prompt playgrounds, and dataset generation into a single unified loop.
• Confident AI (DeepEval) : Best for a massive out-of-the-box metric catalog. It provides 50+ ready-to-use, LLM-graded evaluation metrics (implementing G-Eval and QAG methods) that make setting up an LLM judge instantly actionable without writing complex custom evaluation prompts.
• Langfuse : Best open-source and self-hostable choice. If data privacy or an MIT license is a strict requirement, Langfuse lets you run LLM-based evaluations and track scores alongside application tracing seamlessly.
• Arize Phoenix : Best local-first, free tool. It is lightweight, integrates smoothly with standard OpenTelemetry tracing, and runs locally on a laptop or in a container to help you test and evaluate evaluations with zero initial license friction.
• Galileo AI : Best for judge calibration and managing cost. It includes specialized tools for catching LLM-judge bias, handling evaluation data at scale, and optimizing the cost of running heavy judge models.

To help narrow down which of these fits your setup, tell me:

• Are you looking for an open-source library to run locally in code, or a hosted SaaS platform?
• What framework are you using for your application (e.g., LlamaIndex, LangChain, or custom code)?

If your main problem is “I don’t have enough labeled evaluation data”, I’d pick Braintrust as the first platform to try.

The important distinction is that you don't necessarily need a big ground-truth dataset to get started. A good platform should let you:

1. Collect real production traces.
2. Use an LLM-as-a-judge to score them against a rubric.
3. Have humans review a subset of those scores.
4. Turn disagreements/failures into your evaluation dataset.
5. Re-run the eval whenever you change the prompt/model/agent.

That workflow is particularly important when you start with little or no labeled data.

My shortlist

Why Braintrust: its workflow combines LLM judges, human review, datasets, tracing, and regression/CI evaluation rather than treating them as separate things. That makes it particularly suited to the chicken-and-egg problem of “I need evaluation data in order to build evaluation data.”

LangSmith is probably the strongest alternative. It explicitly supports starting from production traces, sampling interesting runs into datasets, bootstrapping labels with LLM judges, and then refining them with human annotation. LangChain Docs by LangChain Its LLM-as-judge system also supports custom rubrics, few-shot examples, and human corrections to improve judge alignment.

The workflow I'd recommend for you

Don't wait until you have 1,000 labeled examples.

Start with something like:

Production traces → LLM judge → 50–100 human reviews → calibrated judge → growing eval set

For example, suppose you're building a customer-support agent. Your judge could score:

• Correctness — Did it give the right answer?
• Completeness — Did it address all parts of the question?
• Policy adherence — Did it follow your business rules?
• Helpfulness — Would the customer consider this a useful response?
• Hallucination — Did it invent information?

You then manually inspect, say, 50–100 examples, particularly the cases where the judge has low confidence or gives surprising scores. Those become your initial "gold-ish" dataset. You can subsequently measure how well the judge agrees with humans and iterate on the rubric.

One important caveat: LLM-as-a-judge isn't automatically ground truth. You should calibrate it against human judgments, and pair it with deterministic checks wherever possible. LangSmith, for example, explicitly supports human corrections/few-shot examples for this purpose.

If you tell me what you're evaluating (RAG, chatbot, coding agent, voice agent, extraction, etc.) and roughly how many production examples you have, I can tell you which of Braintrust vs. LangSmith vs. Phoenix vs. Langfuse I'd choose and how I'd set up the initial judge.

Don't wait until you have 1,000 labeled examples.

Start with something like:

Production traces → LLM judge → 50–100 human reviews → calibrated judge → growing eval set

For example, suppose you're building a customer-support agent. Your judge could score:

If your biggest problem is “I don’t have good evaluation data”, I’d choose LangSmith first, with Braintrust as the strongest alternative.

The key distinction is that you don't just need an LLM-as-a-judge. You need a platform that helps you turn messy production examples into an evaluation dataset, have the LLM judge them, and then improve/calibrate that judge with human feedback.

My ranking for your situation

LangSmith specifically supports offline evaluation against datasets, online evaluation of production traffic, LLM-as-a-judge, and human calibration of judges.

Why I'd pick LangSmith for your problem

If you don't have a good labeled dataset, I'd set up this loop:

Production traffic → sample real interactions → LLM judge → human review of disagreements → curated eval set → regression testing

That's more valuable than simply uploading a 100-example dataset and asking an LLM to score it.

LangSmith explicitly supports using production traces as the basis for evaluation and calibrating LLM judges against human feedback.

Braintrust is probably my #1 alternative if your primary goal is evaluation rather than observability. Its workflow is particularly strong around datasets, experiments, scorers, human review, and CI/CD quality gates.

One important caveat

Don't assume that “LLM-as-a-judge” solves the lack of ground truth.

If you have no evaluation data, your first goal should be to establish what “good” means. Otherwise the judge can confidently reinforce the wrong standard.

I'd start with perhaps 50–200 real examples, have humans label a subset, and use those to calibrate the judge. Then continuously add examples where:

• the judge and human disagree
• users give negative feedback
• the model produces an obvious failure
• a prompt/model change causes a regression

That gives you an evaluation dataset that gets better over time rather than requiring you to manufacture one upfront.

If you tell me what you're evaluating (RAG chatbot, coding agent, customer-support agent, extraction, voice agent, etc.) and roughly how many production examples you have, I can recommend the specific platform and LLM-as-judge setup I'd use, including the evaluation criteria.

Why I'd pick LangSmith for your problem

If you don't have a good labeled dataset, I'd set up this loop:

Production traffic → sample real interactions → LLM judge → human review of disagreements → curated eval set → regression testing

That's more valuable than simply uploading a 100-example dataset and asking an LLM to score it.

LangSmith explicitly supports using production traces as the basis for evaluation and calibrating LLM judges against human feedback.

Braintrust is probably my #1 alternative if your primary goal is evaluation rather than observability. Its workflow is particularly strong around datasets, experiments, scorers, human review, and CI/CD quality gates.

One important caveat

Don't assume that “LLM-as-a-judge” solves the lack of ground truth.

If you have no evaluation data, your first goal should be to establish what “good” means. Otherwise the judge can confidently reinforce the wrong standard.

I'd start with perhaps 50–200 real examples, have humans label a subset, and use those to calibrate the judge. Then continuously add examples where:

When you lack good evaluation data, the standard industry pattern is to bootstrap your evaluation datasets using a combination of production traffic (traces) and LLM-as-a-judge scorers.

The top platforms specializing in LLM-as-a-judge, synthetic data generation, and evaluation loops fall into a few clear categories depending on your workflow:

Top LLM-as-a-Judge Evaluation Platforms

• Braintrust: Widely considered a top choice overall for an integrated evaluation workspace. It treats datasets, automated LLM-as-a-judge scorers, CI/CD quality gates, and human-in-the-loop review as a unified loop. It's excellent if you need to rapidly spin up experiments and iterate on prompts when your baseline data is scarce.
• Arize Phoenix: The go-to if you want a powerful, local-first, open-source tool. Built by Arize, Phoenix excels at notebook-based evaluation, tracing, and spinning up pre-built LLM-as-a-judge evaluators (for things like hallucination, QA correctness, and toxicity) directly on your data without forcing you into a strict SaaS ecosystem.
• Confident AI (DeepEval): Best if you want a massive catalog of out-of-the-box, research-backed LLM-evaluated metrics (using G-Eval and custom rubrics). It is code-first and integrates smoothly into unit-testing frameworks, making it easy to turn production inputs into test cases.
• Langfuse: The top open-source (MIT-licensed) option if you want to self-host your data and combine production tracing with annotation and LLM evaluation. It allows you to pull production traces and turn them into evaluation datasets seamlessly.
• Galileo: Best if you are worried about the cost and systemic bias of judge models at scale. Galileo features specialized judge calibration and custom evaluator architectures (like Luna) designed to make LLM-as-a-judge scoring cheaper and more faithful.

How to solve the "No Data" problem with these platforms:

1. Instrument Tracing: Hook up an open-source tracer like Arize Phoenix or Langfuse to log real or sandbox user queries.
2. Bootstrap a Dataset: Pull a representative sample of those uncaught real-world inputs into a workspace like Braintrust.
3. Deploy LLM-as-a-Judge: Write plain-language rubrics (e.g., "Is this answer grounded strictly in the provided context?" ) and run a stronger model (like GPT-4o or Claude 3.5 Sonnet) over that dataset to generate baseline scores and synthetic labels.

To help narrow down the ideal platform for you, let me know:

• Are you looking for an open-source/self-hosted tool or a managed cloud SaaS?
• Do you need to capture data from production traffic or build a test suite from scratch?

If your main problem is “I don’t have good labeled evaluation data”, I’d start with langchain.com or braintrust.dev.

My ranking for your specific problem

1. LangSmith — best overall

• Particularly good at the workflow of collect production traces → sample interesting cases → have an LLM judge bootstrap labels → have humans correct/calibrate those labels → turn them into a reusable eval set.
• It explicitly supports starting without a labeled dataset this way.
• Good if you're building agents, RAG, chatbots, or arbitrary LLM applications—not just LangChain apps.

2. Braintrust — best if you want an eval-first workflow

• Excellent for datasets → experiments → LLM-as-judge scorers → regression testing.
• A particularly nice feature is the loop of production traces → interesting examples → dataset → offline evaluation.
• I'd choose this if your primary activity is iterating on prompts/models and measuring whether each change actually improves quality.

3. Arize Phoenix — best open-source option

• Strong if you want to own the infrastructure.
• It supports LLM judges, pre-built evaluators, datasets/experiments, and importantly lets you turn production traces into evaluation datasets.
• Phoenix is also built around OpenTelemetry/OpenInference, which makes it attractive if you don't want to get locked into an evaluation vendor.

The important distinction

An LLM-as-a-judge doesn't actually solve the lack of evaluation data by itself. What you want is a platform that helps you create a flywheel:

Production traffic → interesting examples → LLM judge → small amount of human review → calibrated eval set → regression tests → more production examples

That's why I'd favor LangSmith or Braintrust over a pure evaluation library in your situation. Current platform comparisons similarly distinguish these products by their ability to connect traces, datasets, experiments, human review, and production evaluation.

One caution: don't blindly trust the judge. LLM-as-judge scores need calibration against human judgments; pairwise comparisons can also be easier and more reliable than asking a judge for an absolute 1–10 score.

If you tell me what you're evaluating (e.g. RAG, customer-support chatbot, coding agent, extraction, voice agent, etc.) and roughly how much traffic you have, I can tell you which of LangSmith vs Braintrust vs Phoenix I'd pick and how I'd set up the evaluation loop.

When you lack a golden dataset, LLM-as-a-judge is the standard workaround. Instead of comparing outputs to a hardcoded ground truth, you use a more capable model (like GPT-4o, Claude 3.5 Sonnet, or specialized evaluators) to grade your system's outputs based on criteria like relevance, correctness, hallucination, or tone.

The "best" platform depends on whether you want an evaluation-first framework (great for building datasets and CI/CD testing from scratch) or an observability-first platform (great if you are logging production traffic and want to score it later).

The top platforms specializing in or heavily featuring robust LLM-as-a-judge capabilities include:

• Braintrust — Excellent for evaluation-first workflows. It treats LLM evals like unit tests, makes it remarkably easy to spin up custom LLM-as-a-judge scorers, and handles dataset versioning smoothly. It is widely favored by teams trying to systematically evaluate prompts and models before they touch production.
• Confident AI (DeepEval) — An open-source-backed framework that specializes heavily in unit-testing LLM applications. It features advanced LLM-as-a-judge metrics out-of-the-box (like G-Eval, which lets you define custom criteria in plain English and lets the judge figure out the scoring rubric). Ideal if you want a developer-first, code-heavy testing suite.
• Arize Phoenix — An exceptional source-available/open-source toolkit for evaluation and tracing. It provides powerful prebuilt LLM evaluators (for QA correctness, hallucination, toxicity, etc.) and seamlessly integrates evaluation into both offline experimentation and production monitoring.
• LangSmith — Built by the LangChain team, LangSmith shines brightly if you need deep tracing coupled with run-level LLM-as-a-judge evaluation. It is observability-first, meaning it captures everything flowing through your app and allows you to set up automated evaluators to grade those traces.
• Promptfoo — A lightweight, highly pragmatic CLI-first tool great for CI/CD pipelines. If you need to quickly bombard a prompt or agent with test cases and use an LLM judge to pass/fail builds without setting up a massive enterprise platform, this is a fast favorite.

To help narrow down the ideal choice, tell me:

• Are you looking for an open-source library you can run locally or a hosted enterprise platform?
• Is your primary goal offline experimentation (before deployment) or monitoring live production data?
• What specific type of LLM app are you building (e.g., RAG, conversational agent, structured data extractor)?

The best overall platform for implementing an LLM-as-a-judge when you lack good evaluation data is Langfuse or Braintrust . These tools are highly regarded because they excel at referenceless evaluation —meaning they can evaluate your production logs for semantic relevance, helpfulness, and safety without needing a pre-labeled "ground truth" dataset.

Here is a breakdown of the top platforms tailored specifically to solving your lack of data:

Top Platforms for Referenceless LLM Judges

• Langfuse (Best Open-Source/Self-Hosted) : This platform allows you to extract real user interactions from your logs, format them, and pipe them directly into model-based evaluations. It supports customized rubrics using any LLM provider as the judge.
• Braintrust (Best Enterprise/Iterative Playground) : Braintrust provides an AI-driven playground where you can test your scoring prompts against live outputs. It bridges the gap between having zero data and creating a solid gold-standard evaluation set over time.
• Arize Phoenix (Best Open-Source for RAG) : It features highly optimized, built-in referenceless evaluators that measure hallucination rates (hallucination vs. truthfulness) and QA correctness using only the retrieved context and the final answer.
• Confident AI's DeepEval (Best Developer-First Framework) : A unit-testing framework for LLM applications. It includes G-Eval , a framework that uses Chain-of-Thought (CoT) to automatically generate evaluation steps based on a simple natural language prompt.

Comparison of Top Evaluation Platforms

How to Bootstrap Evals When You Have No Data

To help narrow this down, what type of LLM application are you building (e.g., a RAG chatbot, text summarizer, autonomous agent)? Knowing your tech stack or if you prefer a UI-hosted platform vs. a purely programmatic Python library would also help select the absolute best fit.

If your main problem is “I don’t have enough labeled evaluation data,” I’d pick LangSmith as the best overall starting point.

smith.langchain.com has a particularly good workflow for your situation because it lets you start with LLM-as-a-judge rather than requiring a large ground-truth dataset. You can define a rubric, have an LLM score outputs, inspect failures, and then use human corrections to improve the judge.

My ranking

The important distinction is that LLM-as-a-judge doesn't magically solve the lack of evaluation data. You still need some notion of what “good” means. But you can bootstrap that much more cheaply than creating thousands of labeled examples.

What I'd do in your shoes

Start with maybe 50–100 representative real examples, not thousands.

For each example:

1. Have your application produce an answer.
2. Have an LLM judge it against a detailed rubric.
3. Give the judge a 1–5 score plus reasoning.
4. Manually inspect a subset of the judge's decisions.
5. Correct the judge when it gets something wrong.
6. Feed those corrections back into the evaluator as few-shot examples.
7. Use the resulting evaluator to score new production examples.
8. Turn particularly interesting failures into your permanent eval set.

LangSmith explicitly supports this human-correction → few-shot-example workflow.

It also supports reference-free judging, which is particularly useful when you don't have ground-truth answers.

One important caveat

I'd not start by asking the judge:

“Is this answer good? Give it a score from 1–10.” That's surprisingly unreliable.

Instead, make the judge evaluate specific dimensions, e.g.:

• Does it answer the user's actual question?
• Is every factual claim supported by the provided context?
• Does it follow the requested format?
• Does it omit important information?
• Does it contain unsupported claims?
• Overall: pass/fail.

Then periodically compare the judge against human ratings. The goal isn't merely to get an LLM-generated number; it's to establish that the number correlates with what you actually care about. LangSmith has tooling specifically for auditing and aligning judge scores with human feedback.

If you tell me what you're evaluating (RAG, chatbot, coding agent, customer-support agent, extraction, etc.), I can recommend the specific platform and show you exactly how I'd build the eval when you have almost no labeled data.

• Does it answer the user's actual question?
• Is every factual claim supported by the provided context?
• Does it follow the requested format?
• Does it omit important information?
• Does it contain unsupported claims?
• Overall: pass/fail.

Then periodically compare the judge against human ratings. The goal isn't merely to get an LLM-generated number; it's to establish that the number correlates with what you actually care about. LangSmith has tooling specifically for auditing and aligning judge scores with human feedback.