LangSmith makes building high-quality evaluations easy. This guide explains the key concepts of the LangSmith evaluation framework. The building blocks of the LangSmith framework are:

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Datasets

A dataset is a collection of examples used for evaluating an application. An example is a test input, reference output pair. Dataset

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Examples

Each example consists of:
  • Inputs: a dictionary of input variables to pass to your application.
  • Reference outputs (optional): a dictionary of reference outputs. These do not get passed to your application, they are only used in evaluators.
  • Metadata (optional): a dictionary of additional information that can be used to create filtered views of a dataset.
Example

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Dataset curation

There are various ways to build datasets for evaluation, including:

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Manually curated examples

This is how we typically recommend people get started creating datasets. From building your application, you probably have some idea of what types of inputs you expect your application to be able to handle, and what “good” responses may be. You probably want to cover a few different common edge cases or situations you can imagine. Even 10-20 high-quality, manually-curated examples can go a long way.

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Historical traces

Once you have an application in production, you start getting valuable information: how are users actually using it? These real-world runs make for great examples because they’re, well, the most realistic! If you’re getting a lot of traffic, how can you determine which runs are valuable to add to a dataset? There are a few techniques you can use:
  • User feedback: If possible - try to collect end user feedback. You can then see which datapoints got negative feedback. That is super valuable! These are spots where your application did not perform well. You should add these to your dataset to test against in the future.
  • Heuristics: You can also use other heuristics to identify “interesting” datapoints. For example, runs that took a long time to complete could be interesting to look at and add to a dataset.
  • LLM feedback: You can use another LLM to detect noteworthy runs. For example, you could use an LLM to label chatbot conversations where the user had to rephrase their question or correct the model in some way, indicating the chatbot did not initially respond correctly.

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Synthetic data

Once you have a few examples, you can try to artificially generate some more. It’s generally advised to have a few good hand-crafted examples before this, as this synthetic data will often resemble them in some way. This can be a useful way to get a lot of datapoints, quickly.

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Splits

When setting up your evaluation, you may want to partition your dataset into different splits. For example, you might use a smaller split for many rapid and cheap iterations and a larger split for your final evaluation. In addition, splits can be important for the interpretability of your experiments. For example, if you have a RAG application, you may want your dataset splits to focus on different types of questions (e.g., factual, opinion, etc) and to evaluate your application on each split separately. Learn how to create and manage dataset splits.

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Versions

Datasets are versioned such that every time you add, update, or delete examples in your dataset, a new version of the dataset is created. This makes it easy to inspect and revert changes to your dataset in case you make a mistake. You can also tag versions of your dataset to give them a more human-readable name. This can be useful for marking important milestones in your dataset’s history. You can run evaluations on specific versions of a dataset. This can be useful when running evaluations in CI, to make sure that a dataset update doesn’t accidentally break your CI pipelines.

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Evaluators

Evaluators are functions that score how well your application performs on a particular example.

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Evaluator inputs

Evaluators receive these inputs:
  • Example: The example(s) from your Dataset. Contains inputs, (reference) outputs, and metadata.
  • Run: The actual outputs and intermediate steps (child runs) from passing the example inputs to the application.

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Evaluator outputs

An evaluator returns one or more metrics. These should be returned as a dictionary or list of dictionaries of the form:
  • key: The name of the metric.
  • score | value: The value of the metric. Use score if it’s a numerical metric and value if it’s categorical.
  • comment (optional): The reasoning or additional string information justifying the score.

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Defining evaluators

There are a number of ways to define and run evaluators:
  • Custom code: Define custom evaluators as Python or TypeScript functions and run them client-side using the SDKs or server-side via the UI.
  • Built-in evaluators: LangSmith has a number of built-in evaluators that you can configure and run via the UI.
You can run evaluators using the LangSmith SDK (Python and TypeScript), via the Prompt Playground, or by configuring Rules to automatically run them on particular tracing projects or datasets.

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Evaluation techniques

There are a few high-level approaches to LLM evaluation:

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Human

Human evaluation is often a great starting point for evaluation. LangSmith makes it easy to review your LLM application outputs as well as the traces (all intermediate steps). LangSmith’s annotation queues make it easy to get human feedback on your application’s outputs.

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Heuristic

Heuristic evaluators are deterministic, rule-based functions. These are good for simple checks like making sure that a chatbot’s response isn’t empty, that a snippet of generated code can be compiled, or that a classification is exactly correct.

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LLM-as-judge

LLM-as-judge evaluators use LLMs to score the application’s output. To use them, you typically encode the grading rules / criteria in the LLM prompt. They can be reference-free (e.g., check if system output contains offensive content or adheres to specific criteria). Or, they can compare task output to a reference output (e.g., check if the output is factually accurate relative to the reference). With LLM-as-judge evaluators, it is important to carefully review the resulting scores and tune the grader prompt if needed. Often it is helpful to write these as few-shot evaluators, where you provide examples of inputs, outputs, and expected grades as part of the grader prompt. Learn about how to define an LLM-as-a-judge evaluator.

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Pairwise

Pairwise evaluators allow you to compare the outputs of two versions of an application. Think LMSYS Chatbot Arena - this is the same concept, but applied to AI applications more generally, not just models! This can use either a heuristic (“which response is longer”), an LLM (with a specific pairwise prompt), or human (asking them to manually annotate examples). When should you use pairwise evaluation? Pairwise evaluation is helpful when it is difficult to directly score an LLM output, but easier to compare two outputs. This can be the case for tasks like summarization - it may be hard to give a summary an absolute score, but easy to choose which of two summaries is more informative. Learn how run pairwise evaluations.

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Experiment

Each time we evaluate an application on a dataset, we are conducting an experiment. An experiment contains the results of running a specific version of your application on the dataset. To understand how to use the LangSmith experiment view, see how to analyze experiment results. Experiment view Typically, we will run multiple experiments on a given dataset, testing different configurations of our application (e.g., different prompts or LLMs). In LangSmith, you can easily view all the experiments associated with your dataset. Additionally, you can compare multiple experiments in a comparison view. Comparison view

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Experiment configuration

LangSmith supports a number of experiment configurations which make it easier to run your evals in the manner you want.

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Repetitions

Running an experiment multiple times can be helpful since LLM outputs are not deterministic and can differ from one repetition to the next. By running multiple repetitions, you can get a more accurate estimate of the performance of your system. Repetitions can be configured by passing the num_repetitions argument to evaluate / aevaluate (Python, TypeScript). Repeating the experiment involves both re-running the target function to generate outputs and re-running the evaluators. To learn more about running repetitions on experiments, read the how-to-guide.

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Concurrency

By passing the max_concurrency argument to evaluate / aevaluate, you can specify the concurrency of your experiment. The max_concurrency argument has slightly different semantics depending on whether you are using evaluate or aevaluate.
evaluate
The max_concurrency argument to evaluate specifies the maximum number of concurrent threads to use when running the experiment. This is both for when running your target function as well as your evaluators.
aevaluate
The max_concurrency argument to aevaluate is fairly similar to evaluate, but instead uses a semaphore to limit the number of concurrent tasks that can run at once. aevaluate works by creating a task for each example in the dataset. Each task consists of running the target function as well as all of the evaluators on that specific example. The max_concurrency argument specifies the maximum number of concurrent tasks, or put another way - examples, to run at once.

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Caching

Lastly, you can also cache the API calls made in your experiment by setting the LANGSMITH_TEST_CACHE to a valid folder on your device with write access. This will cause the API calls made in your experiment to be cached to disk, meaning future experiments that make the same API calls will be greatly sped up.

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Annotation queues

Human feedback is often the most valuable feedback you can gather on your application. With annotation queues you can flag runs of your application for annotation. Human annotators then have a streamlined view to review and provide feedback on the runs in a queue. Often (some subset of) these annotated runs are then transferred to a dataset for future evaluations. While you can always annotate runs inline, annotation queues provide another option to group runs together, specify annotation criteria, and configure permissions. Learn more about annotation queues and human feedback.

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Offline evaluation

Evaluating an application on a dataset is what we call “offline” evaluation. It is offline because we’re evaluating on a pre-compiled set of data. An online evaluation, on the other hand, is one in which we evaluate a deployed application’s outputs on real traffic, in near realtime. Offline evaluations are used for testing a version(s) of your application pre-deployment. You can run offline evaluations client-side using the LangSmith SDK (Python and TypeScript). You can run them server-side via the Prompt Playground or by configuring automations to run certain evaluators on every new experiment against a specific dataset. Offline

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Benchmarking

Perhaps the most common type of offline evaluation is one in which we curate a dataset of representative inputs, define the key performance metrics, and benchmark multiple versions of our application to find the best one. Benchmarking can be laborious because for many use cases you have to curate a dataset with gold-standard reference outputs and design good metrics for comparing experimental outputs to them. For a RAG Q&A bot this might look like a dataset of questions and reference answers, and an LLM-as-judge evaluator that determines if the actual answer is semantically equivalent to the reference answer. For a ReACT agent this might look like a dataset of user requests and a reference set of all the tool calls the model is supposed to make, and a heuristic evaluator that checks if all of the reference tool calls were made.

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Unit tests

Unit tests are used in software development to verify the correctness of individual system components. Unit tests in the context of LLMs are often rule-based assertions on LLM inputs or outputs (e.g., checking that LLM-generated code can be compiled, JSON can be loaded, etc.) that validate basic functionality. Unit tests are often written with the expectation that they should always pass. These types of tests are nice to run as part of CI. Note that when doing so it is useful to set up a cache to minimize LLM calls (because those can quickly rack up!).

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Regression tests

Regression tests are used to measure performance across versions of your application over time. They are used to, at the very least, ensure that a new app version does not regress on examples that your current version correctly handles, and ideally to measure how much better your new version is relative to the current. Often these are triggered when you are making app updates (e.g. updating models or architectures) that are expected to influence the user experience. LangSmith’s comparison view has native support for regression testing, allowing you to quickly see examples that have changed relative to the baseline. Regressions are highlighted red, improvements green. Comparison view

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Backtesting

Backtesting is an approach that combines dataset creation (discussed above) with evaluation. If you have a collection of production logs, you can turn them into a dataset. Then, you can re-run those production examples with newer application versions. This allows you to assess performance on past and realistic user inputs. This is commonly used to evaluate new model versions. Anthropic dropped a new model? No problem! Grab the 1000 most recent runs through your application and pass them through the new model. Then compare those results to what actually happened in production.

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Pairwise evaluation

For some tasks it is easier for a human or LLM grader to determine if “version A is better than B” than to assign an absolute score to either A or B. Pairwise evaluations are just this — a scoring of the outputs of two versions against each other as opposed to against some reference output or absolute criteria. Pairwise evaluations are often useful when using LLM-as-judge evaluators on more general tasks. For example, if you have a summarizer application, it may be easier for an LLM-as-judge to determine “Which of these two summaries is more clear and concise?” than to give an absolute score like “Give this summary a score of 1-10 in terms of clarity and concision.” Learn how run pairwise evaluations.

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Online evaluation

Evaluating a deployed application’s outputs in (roughly) realtime is what we call “online” evaluation. In this case there is no dataset involved and no possibility of reference outputs — we’re running evaluators on real inputs and real outputs as they’re produced. This is useful for monitoring your application and flagging unintended behavior. Online evaluation can also work hand-in-hand with offline evaluation: for example, an online evaluator can be used to classify input questions into a set of categories that can later be used to curate a dataset for offline evaluation. Online evaluators are generally intended to be run server-side. LangSmith has built-in LLM-as-judge evaluators that you can configure, or you can define custom code evaluators that are also run within LangSmith. Online

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Testing

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Evaluations vs testing

Testing and evaluation are very similar and overlapping concepts that often get confused. An evaluation measures performance according to a metric(s). Evaluation metrics can be fuzzy or subjective, and are more useful in relative terms than absolute ones. That is, they’re often used to compare two systems against each other rather than to assert something about an individual system. Testing asserts correctness. A system can only be deployed if it passes all tests. Evaluation metrics can be turned into tests. For example, you can write regression tests to assert that any new version of a system must outperform some baseline version of the system on the relevant evaluation metrics. It can also be more resource efficient to run tests and evaluations together if your system is expensive to run and you have overlapping datasets for your tests and evaluations. You can also choose to write evaluations using standard software testing tools like pytest or vitest/jest out of convenience.

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Using pytest and Vitest/Jest

The LangSmith SDKs come with integrations for pytest and Vitest/Jest. These make it easy to:
  • Track test results in LangSmith
  • Write evaluations as tests
Tracking test results in LangSmith makes it easy to share results, compare systems, and debug failing tests. Writing evaluations as tests can be useful when each example you want to evaluate on requires custom logic for running the application and/or evaluators. The standard evaluation flows assume that you can run your application and evaluators in the same way on every example in a dataset. But for more complex systems or comprehensive evals, you may want to evaluate specific subsets of your system with specific types of inputs and metrics. These types of heterogenous evals are much easier to write as a suite of distinct test cases that all get tracked together rather than using the standard evaluate flow. Using testing tools is also helpful when you want to both evaluate your system’s outputs and assert some basic things about them.