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 AI applications is notoriously tricky. Developers need reliable ways to assess whether their generative AI tools actually work as intended, beyond just gut feelings or manual checks.
LangSmith might have cracked part of this challenge. The company has developed a novel approach to evaluating AI app outputs that could dramatically simplify quality control for developers building complex language models.
Their solution? Micro-evaluators - tiny, targeted functions designed to grade application responses with remarkable precision. These aren't just broad, sweeping assessments, but laser-focused tools that can check everything from exact text matches to nuanced linguistic quality.
The implications are significant for an industry drowning in AI uncertainty. Imagine being able to quickly verify if your chatbot, research assistant, or coding companion is delivering accurate, consistent results - without hours of manual review.
But how exactly do these micro-evaluators work? And what makes them so potentially game-changing for AI development?
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.
LangSmith's micro-evaluator approach offers developers a nuanced toolkit for assessing AI application performance. These compact functions can range from simple text matching to complex quality assessments using alternative language models.
The flexibility is striking. Developers can craft custom Python or TypeScript evaluation functions, giving them granular control over how their AI outputs are judged. This isn't just about pass/fail metrics - it's about precise, tailored quality checks.
Imagine checking an AI response against an exact expected text, or deploying a sophisticated LLM to analyze the output's deeper qualities. LangSmith makes both scenarios possible through its lightweight evaluator system.
What's compelling is the SDK's adaptability. Whether you need a basic verification or a complex multi-step evaluation, the platform seems designed to accommodate different testing needs. Custom logic meets standardized assessment.
Still, questions remain about how these micro-evaluators perform at scale. But for now, LangSmith provides developers a promising method to systematically grade AI application outputs with unusual precision.
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
Related Reading
LLMs & Generative AI Ant Group unveils Ring-1T, first open-source trillion-parameter reasoning model
LLMs & Generative AI ChatGPT Health Event Shows AI Modernizing Dev Workflows, GitLab Unveils Plans
LLMs & Generative AI Gen AI app sessions up fivefold, downloads jump 778% as ChatGPT leads traffic
From Archives Evaluating Agentic AI: Transparency, Reliability and Ethics Needed
From Archives OpenAI safeguard models outpace GPT-5-thinking and OSS versions in tests
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.