Editorial illustration for LangSmith's Micro-Evaluators: Grading AI App Outputs with Precision
LangSmith's Smart Eval Functions Test AI App Quality
LangSmith uses tiny evaluator functions to grade app outputs, even simple matches
Testing an AI application isn't like checking if a button works. You're trying to pin down a ghost. Developers have mostly resorted to gut checks and manual reviews, a slow and unreliable way to decide if their chatbot or code assistant is actually performing.
LangSmith thinks it has a better method. The company has built a system of micro-evaluators, small functions that grade an AI app's output. The idea is to replace vague feelings with specific, repeatable checks.
These aren't monolithic testing suites. They are tiny, targeted programs. One might check for an exact text match.
Another could use a separate language model to judge the quality of a response. The goal is precision, not a general thumbs-up.
For an industry struggling to define what "works" even means, this is a practical shift. It means you could automatically verify if your research tool consistently pulls correct dates, or if your customer service bot stays on brand.
LangSmith evaluators are tiny functions (or programs) that grade outputs of your app for a specific example. An evaluator may be as straightforward as verifying if the output is identical to the anticipated text, or as advanced as employing a different LLM to evaluate the output's quality. LangSmith accommodates both custom evaluators and internal ones.
You may create your own Python/TypeScript function to execute any evaluation logic and execute it through the SDK, or utilize LangSmith's internal evaluators within the UI for popular metrics. As an example, LangSmith has some out-of-the-box evaluators for things like similarity comparison, factuality checking, etc., but in this case we will develop a custom one for the sake of example.
The system offers flexibility. You can write a custom function in Python to check for something bizarrely specific to your app. Or you can use LangSmith's built-in tools for common tasks like checking factuality.
This moves evaluation from an art to something closer to engineering. It is a toolkit for building your own definition of quality. The promise is control. Instead of wondering if your AI is good, you define what good means, then automate the test.
Whether this approach holds up under the massive scale of real production traffic is the next question. But for now, it provides a concrete path forward for developers tired of guessing.
Further Reading
- Best AI Evaluation Tools in 2026: Top 5 picks - Maxim AI
- Demystifying evals for AI agents - Anthropic
- 7 best AI observability platforms for LLMs in 2025 - Braintrust - Braintrust
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Common Questions Answered
How do LangSmith's micro-evaluators help developers assess AI application outputs?
Micro-evaluators are tiny, targeted functions designed to grade AI app outputs with precision. They can range from simple text matching to complex quality assessments using alternative language models, providing developers with a flexible and nuanced approach to quality control.
What types of evaluation functions can developers create with LangSmith?
Developers can create custom Python or TypeScript functions to execute specific evaluation logic through the LangSmith SDK. These evaluators can be as simple as checking if an output matches expected text or as advanced as using another LLM to comprehensively assess output quality.
Why are traditional methods of testing AI applications challenging for developers?
Testing AI applications is notoriously difficult because developers cannot rely solely on gut feelings or manual checks. LangSmith's micro-evaluators offer a more systematic and precise approach to verifying whether generative AI tools are working as intended.
Further Reading
- Improve your RAG Results using LangSmith — Focused Labs
- Choosing the Best LLM Evaluation & Monitoring Tool in 2025 — LangWatch Blog
- Best LLM evaluation platforms 2025 — Braintrust
- LangSmith: The Key to Reliable LLM Applications — Nitor Infotech
- Comparing LLM Evaluation Platforms: Top Frameworks for 2025 — Arize
