How to Implement Evals in Your AI Application (A Practical Guide Using LangSmith)

As AI applications move from experiments to production systems, one problem becomes unavoidable: how do you know your system is actually improving?

A prompt that works well today may degrade tomorrow. A model upgrade may silently introduce hallucinations. A new tool integration may cause your agent to behave unpredictably.

This is where evaluation systems (evals) become critical.

In traditional software engineering, we rely on automated tests to ensure reliability. AI systems require a similar discipline. Instead of testing deterministic outputs, we evaluate task performance, reasoning quality, tool usage, and safety behaviors.

This article walks through a practical approach to implementing evals using LangSmith, LangChain's evaluation and observability platform.

The goal is not to build the most sophisticated evaluation framework. The goal is to create a repeatable system that tells you whether your AI application is getting better or worse over time.

A practical eval pipeline: from AI application through structured evaluators to continuous tracking with LangSmith.

The Example We Will Use

To make this discussion concrete, let's assume we are building a customer support assistant for a SaaS company.

The assistant answers product questions by retrieving information from documentation using a RAG pipeline.

The flow looks like this:

User asks a question
The system retrieves relevant documentation
The LLM generates an answer grounded in those documents

At first glance, the system may appear to work well. However, several hidden failure modes can appear in production:

the system retrieves the wrong document
the model hallucinates unsupported information
the answer is technically correct but incomplete
the system fails when question phrasing changes

Without structured evaluation, these problems remain invisible.

This is where LangSmith evaluation workflows become useful.

Step 1: Define What "Success" Means

Before writing evaluation code, the most important step is defining what a correct result actually looks like.

Many teams skip this step and jump directly into testing prompts. But evaluation works best when you clearly define success criteria first.

For our support assistant example, a good response should:

answer the user's question
rely on retrieved documentation
avoid hallucinated information
remain concise and readable

LangSmith evaluations work by measuring these criteria through custom evaluators and grading functions.

Learn more about evaluation concepts in LangSmith.

Diagram showing evaluation criteria layers — User Question flows through Retrieval and LLM Response to Evaluation Checks for Accuracy, Grounding, Completeness, and Clarity — Each response is evaluated across multiple quality dimensions: accuracy, grounding, completeness, and clarity.

Step 2: Create a Dataset of Real Test Cases

Next, we create a dataset of representative tasks.

LangSmith evaluations are dataset-driven. Instead of manually testing prompts, you define a dataset of inputs and run your system against it repeatedly.

LangSmith datasets allow you to store test examples that include:

user inputs
reference outputs
metadata
evaluation criteria

You can learn how to create and manage datasets here.

For our support assistant, the dataset might include questions such as:

"How do I reset my password?"
"What integrations does your platform support?"
"Can I export reports to CSV?"
"Is there an API rate limit?"

In the beginning, 10–20 well-chosen examples are usually enough to build a useful evaluation dataset. Over time, teams add more examples based on real production failures.

Illustration of dataset structure — table showing columns: Question, Expected Answer, Source Document, and Evaluation Criteria with example rows — A well-structured evaluation dataset: questions paired with expected answers, source documents, and evaluation criteria.

Step 3: Run Your Application as the Target Function

LangSmith evaluates systems by executing a target function.

The target function is simply the workflow you want to test. In our example, the function performs:

document retrieval
LLM response generation
returning the final answer

LangSmith runs this function against every dataset example and records the output.

The evaluation framework then compares results across multiple runs.

The official guide for evaluating an application using a target function can be found here.

This design is powerful because it evaluates the entire system, not just the prompt.

Architecture diagram showing User Question flowing through Retrieval System, LLM, and Response to an Evaluation Layer powered by LangSmith — LangSmith evaluates the complete pipeline — retrieval, generation, and response — not just the prompt in isolation.

Step 4: Implement Deterministic Evaluators

The first evaluators you should implement are deterministic checks.

These are evaluation rules written in code instead of using another AI model.

Examples include checking whether:

the response includes document citations
the output format is valid
the response length stays within limits
restricted phrases are avoided

LangSmith allows developers to implement custom evaluators that score outputs programmatically.

Deterministic checks should always be the foundation of an evaluation framework because they are objective and repeatable.

Flowchart showing deterministic checks — Response flows through Citation Check, Format Validation, and Length Check to Pass or Fail — Deterministic evaluators: objective, repeatable checks that form the foundation of any evaluation framework.

Step 5: Use LLM-as-Judge for Complex Evaluation

Some response qualities are difficult to measure using code.

For example:

Is the answer helpful?
Does it fully address the user's question?
Is it grounded in context?

In these cases we can use LLM-as-judge evaluators.

In this approach, another model evaluates the response using a structured rubric.

LangSmith supports building evaluators that use language models as judges.

A typical judge prompt might look like:

"Given the user question, retrieved context, and generated response, determine whether the response is grounded in the provided documents."

This approach allows teams to evaluate qualitative dimensions such as clarity and correctness.

Evaluation loop diagram — Generated Response is sent to an LLM Judge which produces a score: Grounded, Partially Grounded, or Hallucinated — LLM-as-Judge: a language model evaluates qualitative dimensions like helpfulness, grounding, and correctness.

Step 6: Evaluate Intermediate Steps

When building agent systems or complex pipelines, evaluating the final answer alone may not be sufficient.

A system could accidentally produce the correct answer while taking an incorrect reasoning path.

LangSmith allows developers to evaluate intermediate steps in execution traces.

This enables teams to verify:

whether the correct documents were retrieved
whether tools were used correctly
whether the reasoning path followed expected behavior

Trace inspection is one of the most valuable capabilities when debugging AI systems.

Trace visualization diagram — User Question flows through Retrieval, Context, LLM Reasoning, and Response, with evaluation checks at each intermediate step — LangSmith traces let you evaluate every intermediate step, catching issues hidden in the final answer.

Step 7: Turn Evals Into a Continuous Development Loop

The real power of evals comes from running them continuously as the system evolves.

Every time the application changes — whether through prompt adjustments, model upgrades, or retriever improvements — the evaluation dataset should be executed again.

LangSmith makes it easy to compare runs and track improvements over time.

You can learn about evaluation workflows here.

This approach allows teams to detect:

regressions
hallucination increases
retrieval failures
performance improvements

Over time, the evaluation dataset becomes a quality benchmark for your AI system.

Lifecycle diagram — Application Update flows to Run Evals, then Compare Metrics, then Deploy, looping back continuously — The continuous evaluation lifecycle: update, evaluate, compare, deploy, and repeat.

Final Thoughts

AI systems behave differently from traditional deterministic software. Their behavior can shift as prompts, models, or surrounding systems evolve.

Evaluation frameworks like LangSmith introduce the discipline needed to manage that complexity.

A practical evaluation workflow typically includes:

defining clear success criteria
building a dataset of representative tasks
evaluating the entire application pipeline
combining deterministic checks with LLM judges
running evaluations continuously during development

By adopting this approach, AI applications stop behaving like unpredictable prototypes and begin to behave like reliable production systems.