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?

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.

If your main problem is “I don’t have good evaluation data / ground truth”, I’d pick Braintrust as the best overall platform right now.

The important distinction is that you don't actually need a perfect labeled dataset to get started. The better platforms let you:

1. collect real production examples,
2. have an LLM judge score them against a rubric,
3. occasionally have humans review/correct the judge,
4. turn those examples into a reusable evaluation dataset,
5. run the dataset whenever you change your prompt/model/agent.

My ranking

Why I'd start with Braintrust

Braintrust is unusually well aligned with the “I don't have evaluation data yet” problem.

Its workflow is essentially:

production traffic → interesting examples → dataset → LLM-as-judge → human review → better dataset → regression evals

It supports LLM-as-a-judge scorers where you define natural-language criteria such as factuality, helpfulness, correctness, etc. You can create them in the UI or code, and then use them against experiments and production traces.

It also supports human review alongside automated evaluation, which is particularly important when you're building your ground truth rather than starting with it.

The other one I'd seriously consider: LangSmith

LangChain's LangSmith is probably the strongest alternative.

Its LLM-as-a-judge system lets you define rubrics, use reference answers when available, and—crucially for your situation—collect human corrections to judge scores and automatically use those corrections as few-shot examples to improve the evaluator.

That's a very nice answer to:

“How do I make an LLM judge reliable when I don't have a good labeled dataset?”

LangSmith also supports production/online evaluation, so you can evaluate real user interactions and turn those into an increasingly useful evaluation corpus.

One important caveat

I wouldn't just throw your application at an LLM judge and trust its score.

If you have no evaluation data, I'd use a small human-calibrated seed set:

• Collect ~50–200 real examples.
• Write a very explicit rubric.
• Have the LLM judge all of them.
• Manually inspect a meaningful subset.
• Correct the judge's mistakes.
• Iterate on the rubric/judge prompt.
• Keep the human-reviewed examples as your initial “gold-ish” dataset.
• Then use the judge to evaluate thousands of additional examples.

The goal isn't to magically eliminate human labeling. It's to make 50 good human judgments leverage into thousands of automated evaluations.

If you tell me what you're evaluating (RAG, chatbot, coding agent, voice agent, extraction, autonomous agent, etc.), I can narrow this down to the 2–3 platforms I'd actually shortlist for your specific case, including which one is best when you have almost zero labeled data.

When you lack a golden dataset, LLM-as-a-judge is the standard workaround. Instead of comparing outputs against hard ground truth, you use a robust model (like Claude 3.5 Sonnet or GPT-4o) to grade qualitative dimensions like tone, helpfulness, instruction-following, and factual grounding.

The top evaluation platforms that excel at LLM-as-a-judge workflows and dataset bootstrapping include:

• Braintrust (Best Overall for Evals & CI/CD)
  Braintrust treats LLM-as-a-judge as a first-class citizen alongside tracing and data management. It allows you to write custom scorers in code, push them via CLI, or create them in the UI. It is exceptionally good at helping you iterate on scoring rubrics in a playground environment before locking them into your CI/CD pipelines.
• Galileo AI (Best for Judge Consistency & Scale)
  If you are worried about the cost and latency of calling massive LLMs just to grade other LLMs, Galileo features specialized evaluator tooling (like their Luna models) designed to offer high-accuracy evaluation and fast, cost-effective judgment without relying blindly on generic third-party API calls. It explicitly targets judge calibration and bias reduction.
• Confident AI / DeepEval (Best Open-Source/Developer-First Framework)
  If you prefer a code-first, unit-testing approach to your evaluation data, DeepEval provides out-of-the-box, research-backed LLM--as-a-judge metrics (such as G-Eval) that let you define custom evaluation criteria using natural language without needing a pre-existing dataset.
• Langfuse (Best Open Source / Self-Hostable)
  An open-source (MIT licensed) platform that blends production tracing with model-based scoring and annotation. If data privacy is a concern and you want to log production traffic and turn those logs into your evaluation dataset over time using LLM judges, Langfuse is a stellar choice.

To help narrow down the best fit, tell me:

• Are you evaluating a standard RAG application , a multi-step AI agent , or open-ended chat?
• Do you prefer a managed cloud service or an open-source/self-hosted tool?

When you lack good evaluation data, LLM-as-a-judge is the standard approach because it allows you to score, rank, and critique open-ended model outputs without needing thousands of pre-labeled "golden" ground truth answers.

The "best" platform depends heavily on your workflow—whether you want a code-first Python testing library, an open-source self-hosted stack, or an all-in-one enterprise dashboard.

Top LLM-as-a-Judge Platforms

1. Braintrust
   • Best For: Overall evaluation and prompt experimentation workflows used by fast-moving AI product teams [1.22].
   • Why it shines: It bridges the gap between dataset management, tracing, and automated scoring. It makes defining custom LLM-as-a-judge rubrics straightforward while seamlessly merging human-in-the-loop reviews to calibrate your automated judges.
2. DeepEval (by Confident AI)
   • Best For: Developers who want a code-first, unit-testing approach to evals (like pytest for LLMs).
   • Why it shines: It features over 50 ready-to-use, research-backed LLM-as-a-judge metrics (including G-Eval for custom natural-language criteria and pairwise arena comparisons). If you want to run automated checks locally or embed them directly into your CI/CD pipeline, this is a top-tier choice.
3. Maxim AI
   • Best For: Complex AI agents, multi-turn simulations, and cross-functional teams (Product Managers + Engineers).
   • Why it shines: It provides robust pre-deployment simulation alongside flexible LLM-as-a-judge metrics and an accessible no-code UI, meaning non-engineers can easily contribute to shaping and reviewing evaluation criteria.
4. LangSmith (by LangChain)
   • Best For: Teams heavily invested in the LangChain and LangGraph ecosystem [1.215].
   • Why it shines: Offers tightly integrated tracing, debugging, and offline dataset evaluation. Writing custom LLM evaluators and running test suites against trace data is frictionless if your application architecture already uses their framework.
5. Langfuse
   • Best For: Teams that want an open-source (MIT licensed) and easily self-hosted engineering stack.
   • Why it shines: Combines production tracing, prompt management, and native LLM-as-a-judge evaluation methods without forcing you onto proprietary cloud infrastructure.

Pro-Tips for LLM-as-a-Judge When You Have No Data

Since you don't have good baseline evaluation data, your LLM judge needs extra structural help to avoid becoming noisy or biased:

• Use Chain-of-Thought (CoT) Prompting: Always instruct the judge model to write out its reasoning before outputting a numerical score or pass/fail verdict. This dramatically improves alignment with actual human intent.
• Neutralize Position & Verbosity Bias: Judges tend to favor longer answers or the first option in a pairwise comparison. Look for platforms that support automatic position swapping/rotation to keep scores objective.
• Bootstrap with Synthetic Data: If you have raw inputs or user logs, use a tool like DeepEval or Ragas to synthetically generate test cases and reference contexts to jumpstart your evaluation harness.

To help narrow down the ideal choice, let me know:

• Are you looking for an open-source/self-hosted tool or a managed cloud platform?
• What type of application are you testing—a simple RAG pipeline , a multi-turn chatbot , or an autonomous AI agent?

If your main problem is “I don't have enough labeled evaluation data”, I’d pick Braintrust as the best overall platform right now.

My ranking

1. braintrust.dev — best overall for your situation

• Very strong LLM-as-a-judge workflow.
• You can start with a relatively small set of examples and define judges for things like correctness, relevance, helpfulness, factuality, etc.
• Particularly good at turning production examples into datasets → running judges → comparing prompts/models → tracking regressions.
• It supports evals from code, CLI, or UI and has built-in scorers plus custom scorers.
• I'd choose it if your goal is to get from “we have almost no eval data” to “we have a functioning automated evaluation loop” quickly.

2. langchain.com — best if you're building agents

• Excellent for turning real production traces into evaluation datasets.
• Supports LLM-as-judge, human annotation, pairwise comparisons, offline evals, and online evals.
• Particularly interesting for your problem because LangSmith explicitly supports using LLM judges to bootstrap labels and then using human annotation to calibrate them.
• I'd pick this over Braintrust if you're heavily invested in LangGraph/LangChain or have complex multi-step agents.

3. phoenix.arize.com — best open-source option

• Very good if you want to own the evaluation infrastructure.
• Has LLM evaluators for correctness, relevance, hallucination/faithfulness, etc.
• More engineering-oriented than Braintrust/LangSmith, so I'd expect you to build more of the evaluation workflow yourself.

But there's an important distinction

An LLM-as-a-judge doesn't solve the “no evaluation data” problem by itself.

The really valuable capability is:

Production data → sample representative cases → LLM judge → human review of a subset → calibrated judge → growing gold dataset That's the loop I'd optimize for.

For example, suppose you have 10,000 historical customer conversations but only 50 manually labeled examples. You can:

10,000 production examples
          ↓
   sample 500 cases
          ↓
    LLM-as-a-judge
          ↓
   50–100 human reviews
          ↓
 compare judge vs humans
          ↓
 refine judge criteria/prompt
          ↓
    automated evaluation
          ↓
 continuously add difficult cases

This is much more useful than simply buying a platform with 50 different evaluation metrics.

For your specific problem, I'd probably start with Braintrust and LangSmith head-to-head. Current comparisons also put Braintrust ahead when the primary workflow is structured evaluation/experimentation, while LangSmith has an advantage when production traces, agent debugging, and human calibration are central.

If you tell me what you're evaluating (chatbot, RAG, coding agent, customer support, extraction, etc.) and roughly how many production examples you have, I can tell you which one I'd choose and how I'd bootstrap the eval dataset.

If your core problem is “I don’t have enough labeled evaluation data,” I’d look first at LangSmith.

LangSmith is particularly well suited because it lets you use an LLM-as-a-judge to bootstrap evaluation, while also giving you a path toward collecting the human labels you’re currently missing. It supports both offline evaluations on datasets and online evaluation of production traces.

Why I'd pick LangSmith for your situation

The important distinction is that you don't need a gold-standard answer for every example.

You can define a rubric such as:

“Score the response 1–5 for factual correctness, completeness, and usefulness. Explain the score.”

LangSmith can then have an LLM judge your outputs according to that rubric. It supports reference-free judges for things like helpfulness, clarity, tone, and coherence, as well as reference-based judges when you eventually have ground truth.

More importantly, you can create a human → judge feedback loop:

1. Send your existing production examples through your application.
2. Have an LLM judge score them.
3. Have humans review a small, representative subset.
4. Compare human scores with judge scores.
5. Adjust the judge prompt/rubric.
6. Use the calibrated judge to score much larger quantities of data.
7. Add interesting/failing examples back into your evaluation dataset.

LangSmith explicitly supports this alignment workflow, including annotation queues and using human corrections to improve the judge. Their docs recommend starting with roughly 20 diverse human-labeled examples rather than needing thousands.

The alternatives

The key thing I'd optimize for isn't “which platform has the best LLM judge?” It's “which platform makes it easiest to turn a tiny amount of human feedback into a trustworthy automated evaluator.”

That's why I'd put LangSmith at #1 for your particular problem. It has explicit tooling for auditing judge scores and correcting them, rather than treating the LLM judge as ground truth.

One caveat: don't just run an LLM judge over 10,000 examples and assume the resulting scores are your evaluation dataset. LLM judges have systematic biases. The useful setup is LLM judge → small human sample → measure judge/human agreement → calibrate → scale.

If you tell me what you're evaluating (RAG, agents, customer-support answers, coding agents, extraction, etc.) and roughly how many unlabeled examples you have, I can recommend the platform/workflow much more specifically.

When you don’t have a labeled ground-truth dataset, LLM-as-a-judge is the industry-standard way to bootstrap your evaluations. Rather than looking for a single tool, you should pick a platform based on your workflow (code-first vs. UI-driven, open-source vs. managed enterprise).

The top platforms featuring robust, out-of-the-box LLM-as-a-judge capabilities are categorized below by their strengths:

• Braintrust — Best Overall for End-to-End Evaluation & Experimentation. Excellent for teams that want dataset management, prompt playgrounds, and autoevals/LLM-as-a-judge integrated directly into CI/CD quality gates. It handles logging and scoring seamlessly without forcing you into a rigid framework.
• DeepEval (by Confident AI) — Best Open-Source & Code-First Python Framework. Operates like a pytest suite for LLMs. It includes 50+ research-backed, ready-to-use LLM-as-a-judge metrics (such as G-Eval for custom rubrics and GEval/Arena for pairwise comparisons) that run locally or in your CI/CD pipeline.
• Arize Phoenix — Best for Open-Source Observability & Trace-Based Evals. Maintained by Arize, Phoenix provides powerful client-side and server-side LLM-as-a-judge evaluators. It is natively built on OpenTelemetry, making it ideal if you want to pull semantic evaluation scores directly from your app's live traces.
• Maxim AI — Best for Collaborative & Cross-Functional Teams. Offers comprehensive end-to-end evaluation, simulation, and observability. It stands out by providing a clean no-code/low-code UI that allows product managers and domain experts to build and tweak evaluation rubrics alongside engineering.
• Promptfoo — Best CLI & Red-Teaming Specialist. If your evaluation problem involves stressing test prompts against security vulnerabilities, PII leaks, or cross-model comparisons via the command line, Promptfoo's LLM-driven matrix testing and red-teaming are unmatched.

To help narrow down the right choice, tell me:

• Are you looking for an open-source (self-hosted) tool or a managed cloud platform?
• Is your app a simple RAG pipeline , a multi-turn chatbot , or an autonomous agent?

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

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, then continuously improve that dataset.

My ranking for your situation

Why I'd start with Braintrust

The workflow you want is roughly:

real user interactions → identify bad/good examples → LLM judge → dataset → regression tests → iterate

That's more valuable than simply having a library that says:

“Here's an LLM judge. Give it a prompt and some examples.”

Braintrust is particularly oriented around closing that loop between production traces, datasets, experiments, and evaluations. Its current positioning explicitly emphasizes turning production traces into test cases and using them for regression testing.

So if you currently have little or no labeled eval data, I'd do something like:

1. Log your production inputs/outputs.
2. Sample ~100–500 real interactions.
3. Have an LLM judge score them on 2–5 dimensions:
   • correctness
   • relevance
   • completeness
   • adherence to instructions
   • hallucination / unsupported claims
4. Manually inspect a subset of the judge's decisions.
5. Fix the judge prompt/rubric until it agrees reasonably well with humans.
6. Turn the interesting failures into your golden/regression dataset.
7. Run that dataset every time you change the prompt/model/system.

That's much better than waiting until you have a pristine human-labeled dataset.

One important alternative: LangSmith

If you're already using LangChain or LangGraph, I'd probably choose LangSmith instead.

LangSmith lets you create datasets from curated examples, historical production traces, or synthetic data, and supports LLM-as-a-judge, human review, code evaluators, and pairwise comparisons.

It also has a nice mechanism for calibrating LLM judges against human feedback, which is especially relevant to your problem.

The bigger issue, though

I'd actually challenge the premise that you need to find an evaluation dataset first.

For most LLM applications, the best evaluation data is your own application's traffic. Public benchmarks often aren't representative of what your users actually ask.

And LLM-as-a-judge isn't magic: judges have biases and can disagree with humans. Research has shown issues such as position, verbosity, and self-enhancement bias, which is why a small amount of human calibration is important.

So I'd optimize for a platform that makes this loop easy:

production data → judge → human calibration → curated eval set → automated regression testing

If you tell me what you're building (e.g. RAG chatbot, coding agent, customer support agent, extraction/classification, etc.), I can tell you which of Braintrust vs. LangSmith vs. Langfuse I'd pick and exactly how I'd bootstrap the first 100 eval cases.

When you don't have good evaluation data (golden datasets), LLM-as-a-judge is the exact right strategy to bootstrap your quality loop.

Instead of naming a single "best" platform, the industry has crystallized around a few category leaders depending on your workflow, budget, and data privacy needs.

The top platforms for LLM-as-a-judge evaluation fall into these distinct categories:

• Braintrust is best for eval-first product development and fast iteration . It treats logs, datasets, and LLM-as-a-judge scoring as a unified engineering loop . If an output fails in production, you can push it straight into an experiment dataset with one click . It has a great developer experience and a generous free tier, though advanced self-hosting requires enterprise plans.
• Arize Phoenix (open-source) and Arize AX (enterprise) are best for OpenTelemetry-native tracing and deep production observability . Phoenix is open-source, runs locally via Docker, and lets you spin up powerful LLM evaluators against your application traces instantly . If you need a robust, open-core foundation that scales from local debugging to full enterprise production governance, this is a top-tier choice.
• LangSmith is best if your stack is already anchored in LangChain or LangGraph . It features zero-config auto-instrumentation for those frameworks, making it seamless to log agent trajectories and apply LLM-as-a-judge scorers to multi-step agent behaviors. (Note: It is less ideal if you are trying to avoid vendor lock-in outside the LangChain ecosystem).
• Langfuse is best for teams wanting a fully open-source, self-hostable alternative . It is MIT-licensed, easy to self-host in an afternoon, and bundles prompt management, tracing, and evaluation queues cleanly without forcing you onto a proprietary cloud.
• DeepEval (by Confident AI) is best for code-first, CI/CD-driven unit testing . It operates like a pytest suite for LLMs, letting you script complex LLM-as-a-judge metrics right into your GitHub Actions to block regressions before code merges.

A Quick Reality Check on LLM-as-a-Judge

If you lack evaluation data, remember these rules to keep your automated judge honest:

1. Ditch 1–10 scales: LLMs tend to cluster scores around 7 or 8 (mean-reversion). Use binary (pass/fail) or clear categorical labels (yes/no/insufficient_evidence).
2. Mitigate position and verbosity bias: Judges tend to favor longer answers or the first option in a pairwise comparison . Ensure your platform or prompt rotates candidate positions.
3. Calibrate against yourself: Manually grade 30–50 outputs yourself first, run them through your chosen judge prompt, and make sure the judge agrees with your baseline before trusting it on thousands of unlabelled rows.

To help narrow down which platform fits your situation best, tell me:

• Are you looking for a managed cloud service or an open-source/self-hosted tool?
• Are you evaluating a simple RAG/chatbot pipeline or a multi-step AI agent?