If you're a software engineer shipping an AI product, you already know how to write unit tests and set up CI/CD. But the LLM evaluation process (or "LLM evals") is a different kind of problem. Your outputs are non-deterministic, quality is subjective, and the gap between a demo that works in your notebook and a production system that consistently delivers value is wider than it looks.

Most teams get this wrong early on. They think evals are just about running static test datasets before deployment. That's only half the story. Effective LLM evaluation requires both offline evals (testing against curated datasets during development) and online evals (continuously assessing real production traffic). Think of it like the difference between your test suite and your production monitoring: you need both, and they serve different purposes.

To understand how these fit together in your development process, see our guide on end-to-end agent evaluation and observability.

What makes this harder than traditional testing: there are multiple LLM evaluation methods, each with different trade-offs. Some teams run manual reviews. Others use automated metrics. The best approach usually combines both. And you have to stay on top of performance drift over time.

Here's how to think about it.

What Is LLM Evaluation?

LLM evaluation is the process of systematically testing whether your AI product's LLM outputs meet your requirements for quality, accuracy, safety, and other assessment criteria you define. It's how you verify that your LLM system is actually working as intended, not just running without errors.

Evals are different from monitoring. Monitoring tracks whether your system is up and response times are within limits. Evals tell you whether the outputs are actually good. You need both.

Why Is LLM Evaluation Important?

Shipping an AI product without evals is like deploying code without tests. You might get lucky, but you probably won't.

LLM outputs are non-deterministic. Even with the best prompts, large language models (LLMs) sometimes generate hallucinations, biased outputs, or responses that don't match what you expect. You can't just assert on a return value the way you would with a function. And if you don't know what "good" looks like for your specific use case, how will you know if your system is performing well?

Then there's the production gap. A prompt that works 80% of the time in your notebook might not be good enough when real users depend on it. Your system needs to perform consistently on the model outputs users care about, not just on arbitrary benchmarks. And the model's performance degrades over time as usage patterns evolve and model providers push updates. Without evals, you won't catch that drift until users start complaining.

LLM Model Evaluation vs LLM System Evaluation

There's an important distinction between evaluating base language models and evaluating LLM systems or applications:

Model evaluation assesses the underlying capabilities of a base LLM itself. This is how you understand what foundation models like Claude, GPT-4, Llama, or Gemini can do out of the box.

LLM system evaluation assesses the entire AI product you've built: the prompts, retrieval augmented generation (RAG), tool calling, and orchestration. This is what your users actually interact with, so this is what you should focus on.

Most teams should focus on system evaluation, because that's what determines whether your product works. The underlying model capability matters far less than how well you've set up your system around it.

This gets even more important with agentic systems, where you need to evaluate multiple components: the planner, tool calling, the LLM's ability to recover from errors, and the end-to-end workflow.

To see how evaluation and observability work together for agent-based systems, check out our guide on building production-grade AI agents.

Why Custom LLM Evaluations Are Necessary

Generic benchmarks and off-the-shelf metrics can tell you something about a model's general capabilities, but they won't tell you whether your product is any good. Every LLM application has its own definition of quality, and that definition depends on your users, your proprietary data, and your specific use case.

Consider the difference between a coding assistant and a customer support chatbot. The coding assistant needs to generate syntactically correct, runnable code. The chatbot needs to resolve tickets with the right tone and accurate information. A single evaluation framework can't meaningfully cover both.

This is why custom evals matter. You need to define what "good" looks like for your specific product, then build evals that measure exactly that. Off-the-shelf toxicity scores or generic "helpfulness" ratings might check a box on a dashboard, but they rarely correlate with whether users are actually getting value from your system.

The teams that ship reliable LLM applications are the ones that invest time upfront in defining their own evaluation criteria grounded in real user needs and iterated on as those needs evolve.

Here's a practical starting point: pick the three quality dimensions that matter most to your users. Build manual evals for those first. Once you've labeled enough examples to understand the patterns, start automating. You can expand your eval coverage over time, but starting with a narrow, well-defined scope is better than trying to measure everything at once.

Human-in-the-Loop (HITL) Evaluation

The simplest approach: have human evaluators read the output and judge whether it meets your criteria. Human evaluation is especially important early on when you're still defining what "good" looks like.

Pros:

• Catches nuance and context that automated metrics might miss

• Flexible and easy to customize for your specific use case

• Often, the fastest way to get started

Cons:

• Doesn't scale well

• Introduces human bias

• Gets expensive quickly

Human review isn't something you outgrow. Even teams with sophisticated automated eval pipelines still run human reviews on a regular basis. The role shifts over time: early on, human evaluators define quality criteria and provide feedback through labeled data. Later, they validate that automated evals are still measuring the right things and catch edge cases that automation misses.

The key is building efficient review workflows so human eval doesn't become a bottleneck. Batch reviews, clear rubrics, and good tooling make a big difference here. Think of it like code review: you wouldn't skip it just because you have a linter.

To see how Freeplay helps teams run human reviews faster, check out our guide on writing better evals with Freeplay's AI assistant.

LLM-as-a-Judge and Hybrid Evaluation Approaches

You can use a more powerful LLM (like Claude) to evaluate outputs from another model. This lets you automate evals at scale while maintaining quality that's closer to human judgment than simple rule-based metrics.

How LLM-as-a-judge works: You define your eval criteria in a prompt, feed the model's output to the judge LLM, and get a structured score or assessment back. For example, you might ask an LLM judge to rate whether a response is relevant, complete, and factually grounded on a 1-5 scale.

When to use it:

• When you need to evaluate semantic quality dimensions that are hard to capture with code (like relevance, tone, or coherence)

• When human review is too slow or expensive for the volume you need

• When you want consistent scoring across thousands of examples

The catch: LLM judges have their own biases. They tend to prefer longer, more verbose model outputs. They can be fooled by confident-sounding but incorrect answers. And they occasionally disagree with human reviewers on edge cases.

The best teams use a hybrid approach: LLM-as-a-judge for scale, human review for validation, and code-based checks for anything with a clear right answer (like format compliance or specific keyword inclusion). This combination gives you the speed of automation with the accuracy of human judgment where it counts.

For a deeper look at using LLM judges to evaluate RAG pipelines, see our guide on automatically evaluating RAG prompts and pipelines.

How to Evaluate Large Language Models

At its core, every eval compares outputs against some standard or expectation. The approaches differ in how they do that comparison and what trade-offs they make.

Manual Evaluation

Start with a small dataset (50-100 examples) and manually evaluate them. Define your success criteria very clearly. Once you understand what "good" looks like, you can start automating. Manual eval is the foundation for everything else, and you'll return to it regularly even after building automated pipelines.

Automated Metrics

Automated metrics compare generated outputs against expected outputs. Some common ones:

Exact Match (EM): Did the model output exactly match the expected output?

BLEU Score (Bilingual Evaluation Understudy): Measures how similar the generated text is to the reference text by counting exact word matches against a ground truth. (Not great for LLM evaluation because it's very rigid.)

ROUGE Score (Recall-Oriented Understudy for Gisting Evaluation): Similar to BLEU, but slightly better at capturing semantic similarity.

Semantic Similarity: Using embeddings to measure how similar two pieces of text are conceptually. More flexible than exact word matches for evaluating LLM responses.

These work best for structured tasks (classification, extraction, translation). For open-ended generation, you'll need to layer in LLM-as-a-judge or human review to capture quality dimensions that simple metrics miss.

Reference-Free Evaluation

Not all evaluation requires comparing against a reference answer. You can also evaluate outputs based on properties like:

Factuality: Did the model make factual claims? You can check this with fact-checking models or manually.

Instruction Following: Did the model follow the instructions in the prompt?

Toxicity and Safety: Does the output contain harmful or inappropriate content?

Relevance and Coherence: Is the output relevant and coherent?

For agentic systems, you'd also evaluate whether agents made the right tool calls and recovered from errors effectively.

Code-Based Checks

Code-based checks handle structured validation: does the output match an expected JSON schema? Does it contain required fields? Does it follow formatting rules? These are fast, deterministic, and should be part of every eval pipeline. They catch the obvious failures so your more expensive evaluation methods can focus on the harder quality questions.

The practical approach is to layer these methods. Use code-based checks as a first pass to catch obvious issues. Use automated metrics and LLM-as-a-judge for quality scoring at scale. And use human review to validate everything else and catch what automation misses.

Evaluation During Model Training

If you're fine-tuning a model for your specific use case, evals play a different role during model development. During training, you use evaluation datasets to measure whether the model is learning the right behaviors from your training data. The goal is to make sure your fine-tuning actually improves model accuracy on the tasks that matter to you.

For most teams working with LLMs through APIs (rather than fine-tuning their own), this stage is less relevant. Your eval focus should be on system-level performance: how your prompts, retrieval, and orchestration work together. Reward models and reinforcement learning from human feedback (RLHF) are mostly concerns for teams doing machine learning research, not for teams building LLM applications.

That said, even API-based teams benefit from understanding model-level evals. When a model provider releases a new version, the eval results they publish can help you decide whether to upgrade. And if you're considering fine-tuning down the road, you'll need to set up your own eval pipeline to make sure the fine-tuned model actually improves on the metrics that matter to your product.

Evaluation in Production

Offline evals give you confidence before deployment, but production evals are where you find out what's really happening. Real users interact with your system in ways you didn't anticipate. Edge cases you never thought of show up on day one. If offline evals are your test suite, production evals are your observability layer.

Continuous evaluation involves sampling live traffic, running your eval criteria against those samples, and tracking output quality trends over time. It's how you catch drift, discover new failure modes, and validate that your offline test results actually hold up in the real world.

The gap between offline and production performance is often larger than teams expect. You might achieve 95% success on your curated test set, then discover you're at 70% in production because real users interact with your system in unexpected ways. Without production evals, you'd never know this gap exists.

Key practices for production evaluation:

• Sample systematically. Don't just look at the examples users complain about. Random sampling gives you an unbiased view of overall quality. Stratify your samples by user segment, feature, or model to get a complete picture.

• Run the same evals online and offline. Consistency between your test environment and production monitoring makes it easier to diagnose issues. If a metric drops in production, you can immediately test the same inputs offline to isolate the problem.

• Set up alerts. When quality metrics drop below your baseline, you want to know immediately, not weeks later. Define clear thresholds for cost anomalies, quality degradation, error rate spikes, and latency outliers.

• Feed production insights back into development. When you find failures in production, add those examples to your offline test suite. This creates a feedback loop that makes your evaluations more representative over time.

• Track trends, not just snapshots. A single low score might be noise. A downward trend over a week probably isn't. Build dashboards that show quality metrics over time so you can distinguish signal from noise.

For more on monitoring production LLM systems, check out our guide on how to build LLM evals you can trust.

Key LLM Evaluation Metrics

There's no universal set of LLM evaluation metrics every team should track. The right metrics depend on what you're building. But most teams end up needing some combination of these categories:

The most important are your task-specific quality metrics: does the output actually accomplish what it's supposed to? For a summarization system, that's accuracy and contextual relevance. For a coding assistant, it's whether the code compiles and passes tests. For a chatbot, it's whether the user's question was actually answered.

You'll also need safety and compliance metrics if you're building anything customer-facing: harmful content, PII leakage, policy violations, jailbreak attempts.

Don't ignore operational metrics like latency, token usage, and cost per request. They aren't "evals" in the traditional sense, but a response that's accurate and takes 30 seconds isn't useful. And if you're building agentic systems, track behavioral metrics too: tool call accuracy, decision path quality, loop detection, and task completion rates. These capture what's happening beyond raw text quality.

The worst mistake teams make is tracking metrics that look impressive on a dashboard but don't correlate with actual product quality. The classic example: an LLM evaluation tool that offers just three generic metrics for every use case (Hallucination, Toxicity, and Bias). These might matter for some products, but they're not actionable for most teams and don't capture the specific quality dimensions that differentiate your product.

Focus on metrics that, when improved, would make a real difference to your users. And revisit your metrics regularly. What you need to measure in month one is often different from what matters in month six, as your understanding of user behavior and failure modes evolves. For more on production monitoring, see our guide on LLM observability.

Standard Benchmarks for LLM Evaluation

The AI community has developed several well-known benchmarks for evaluating base model capabilities:

• MMLU (Massive Multitask Language Understanding): Tests natural language understanding across 57 subjects. Useful for comparing general model capabilities, but says little about how a model will perform in your specific application.

• HumanEval: Evaluates code generation ability and the model's reasoning capabilities. Relevant if you're building coding tools, less so for other use cases.

• HellaSwag: Tests commonsense reasoning through sentence completion tasks.

• TruthfulQA: Measures whether models generate truthful answers rather than plausible-sounding but incorrect ones. An important benchmark for model accuracy in factual contexts.

• SQuAD (Stanford Question Answering Dataset): Evaluates reading comprehension by testing whether a model predicts the correct answer from a given passage. Widely cited in natural language processing research papers.

• BLEU and ROUGE: Traditional natural language processing metrics that measure text overlap between generated and reference outputs. Useful for translation and summarization but too rigid for most LLM evaluation scenarios.

These benchmarks are helpful for comparative analysis of foundation models against each other, but they have significant limitations. They test general capabilities, not your specific use case. A model that scores well on MMLU might still perform poorly on your particular task. And benchmark performance doesn't account for how your prompts, retrieval pipeline, or agent architecture affect outputs.

Use benchmarks as a starting point for model selection, but always follow up with custom evaluation tasks that test what actually matters for your product.

What to Look for in an LLM Evaluation Platform

As your eval needs grow, you'll eventually want a platform rather than a collection of scripts and notebooks. The biggest thing to look for is a tool that covers the full eval lifecycle, from development through production, in one place.

That means it should handle both offline and online evals. If your test datasets live in one system and your production monitoring lives in another, you'll spend more time reconciling differences than improving your product. It should also support multiple eval methods (human review, LLM-as-a-judge, and code-based checks) since you'll use all three.

Flexibility matters a lot here. Avoid platforms that only offer generic metrics. You need to define eval criteria specific to your product and use case. And make sure the platform integrates with your production stack: it should ingest logs, support sampling strategies, and show quality trends over time.

A few other things that matter more than you'd think: collaboration features (evals aren't just an engineering task, and domain experts need access too), tight prompt management integration (so you can run your eval suite against new prompt versions before shipping), and clear cost visibility (eval tooling that eats into your LLM budget defeats the purpose).

Top LLM Evaluation Frameworks and Tools

Choosing the right LLM evaluation framework depends on your team's needs, stack, and how much of the evaluation lifecycle you want covered. Here's a look at the leading evaluation tools and platforms.

Freeplay

Freeplay is an end-to-end platform for teams building production AI products. It connects LLM observability, experimentation, and evals into a single workflow so you're not stitching together separate tools for each stage. Freeplay supports model-graded evals, code-based evals, and human review workflows, all connected to both offline test runs and production monitoring. The platform also includes prompt management with version control and instant deployment, so you can test changes against your eval suite before shipping, similar to running your test suite in CI before merging.

Freeplay is built for collaborative teams where engineers, product managers, and domain experts all participate in defining and running evals. You still need to do the work of defining what "good" looks like for your product, but Freeplay gives you the tooling to operationalize those definitions and keep them running at scale. Teams like Postscript use Freeplay to run dozens of custom evals before deploying prompt changes, and Help Scout achieved 75% cost savings while shipping AI features faster using the platform.

LangSmith

LangSmith, built by the LangChain team, provides tracing, evaluation, and monitoring for LLM applications. It's tightly integrated with the LangChain framework, making it a natural choice for teams already using LangChain for orchestration. LangSmith offers dataset management, automated evaluation runs, and a tracing UI for debugging agent workflows. If your stack is built on LangChain, the integration is straightforward. If you're using other frameworks, you'll need to evaluate how well it fits your setup.

Arize AI

Arize focuses on ML observability and has expanded to cover LLM evaluation and monitoring. The platform offers tracing for LLM applications, automated evaluation with pre-built and custom metrics, and production monitoring dashboards. Arize is particularly strong for teams that need to monitor both traditional ML models and LLM applications in a single platform. Their Phoenix open-source library provides a lightweight entry point for teams exploring LLM observability.

Langfuse

Langfuse is an open-source LLM engineering platform that provides tracing, evaluation, and prompt management. It's a good option for teams that want self-hosted observability or prefer open-source tooling. Langfuse supports custom evaluation functions, dataset management, and integrates with multiple LLM frameworks. The self-hosted option gives teams full control over their data, which matters for organizations with strict compliance requirements.

Braintrust

Braintrust offers an evaluation and logging platform for AI applications. It provides tools for running experiments, scoring outputs, and comparing different model configurations. Braintrust emphasizes fast iteration cycles and supports both automated and human evaluation workflows. The platform's experiment-first approach makes it easy to test changes and see results quickly.

Best Practices for LLM Evaluation

We've seen a lot of teams go through this process, and the ones that get the most value from evals tend to follow a few common patterns. None of this is rocket science, but it's easy to skip steps when you're moving fast.

1. Start with manual evals, then scale with automation. Start with a small dataset and evaluate it manually. Define your success criteria very clearly. Once you understand what "good" looks like, you can automate it.

2. Test the right behaviors and edge cases. Don't just test the happy path. Test edge cases, adversarial inputs, and error conditions. Test multilingual inputs if you ship to multiple regions. Test bias.

3. Run evals continuously in production. Set up continuous evals on real production traffic. Compare your system's performance against baseline runs. Build dashboards and alerts so you catch drift early.

4. Version your evals. Keep track of how your eval criteria change over time. This helps you understand whether performance improvements are real or just because you changed your metrics.

5. Use LLM-as-a-Judge for at-scale evals. You can use a more powerful LLM (like Claude) to evaluate outputs from a cheaper or faster model. This lets you automate evals at scale while maintaining quality.

6. Balance human judgment with automation. Use automated metrics to quickly filter large datasets. Use human reviewers for edge cases and final validation. This is the most cost-effective approach at scale.

7. Evaluate tradeoffs between speed, cost, and quality. Every decision involves these three dimensions. Document what you're optimizing for and measure the impact on all three.

Common Challenges in LLM Evaluation

Every team building production AI systems hits the same walls. None of these are unsolvable, but knowing about them upfront helps you plan around them.

The first one is dataset curation. You need test data that reflects your production distribution, but creating this is time-consuming and requires domain expertise. For some use cases, there's a clear right answer. For others, even human experts might disagree about what "good" looks like.

Related: most LLM development teams have never done LLM evals before. Traditional engineers know how to write unit tests, but evaluating LLMs involves unfamiliar processes. The learning curve is steeper than it looks, and deciding what to measure is genuinely hard. A human expert might immediately have a clear sense of output quality, but articulating the criteria they use and turning those into an evaluation framework others can use requires deliberate thought and iteration.

There's also the scaling problem. Human review gives you the most accurate signal, but it's slow and expensive. You need fast feedback without waiting days for review cycles. But you also can't fully automate away human judgment for nuanced quality dimensions.

Then there's the gap between offline and production performance, which is often larger than expected. You might achieve 95% success on your curated test set, then discover you're at 70% in production because real users interact with your system in ways you didn't anticipate.

The industry doesn't make this easier. There are dozens of eval metrics and benchmarks out there, and just because a metric exists or a benchmark is popular doesn't mean it's relevant to what you're building. We've seen teams track metrics that look impressive but have zero correlation with actual product quality.

And finally, there's the ongoing tension between speed and thoroughness. Perfect evals are impossible, and waiting for perfect evals means never shipping. Start with simpler approaches, add sophistication as needed, and focus your most thorough evals on your highest-risk areas.

Conclusion

Evals aren't a one-time gate before launch. They're an ongoing practice, more like testing and code review than a certification you earn and move past.

The pattern that works: offline evals during development to catch issues before they ship, online evals in production to catch the things your test suite missed. Manual review for the hard judgment calls, automation for everything else. And being intentional about what you're measuring and why, because the wrong metrics are worse than no metrics.

The teams that succeed with AI products aren't the ones with perfect eval systems. They're the ones who start with a bounded problem, define what "good" looks like for their specific use case, and build practical evaluation methods to measure it. Start with simple approaches, learn from real production data, and add sophistication as you go.

If you're just getting started, pick one high-value feature in your LLM app and write three manual evals for it. That's enough to build the muscle. From there, you can layer in automation, set up production monitoring, and expand your coverage over time.

If you want help setting up evals for your AI product, we'd be happy to walk you through how Freeplay can fit into your workflow.