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The Definitive Guide to Testing LLM Applications
Engineering teams building and testing LLM applications face unique challenges. The non-deterministic nature of LLMs makes it difficult to review natural language responses for style and accuracy, requiring robust testing with new success metrics.
This guide will help you add rigor to your testing process, so you can iterate faster without risking embarrassing or harmful regressions.
In this guide, you’ll learn:
Tips for testing across the product lifecycle
Methods for building a dataset & defining testing metrics
Templates for evaluating RAG and agents, with visual examples
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Walker Ward
Staff Software Engineer Architect
"LangSmith has made it easier than ever to curate and maintain high-signal LLM testing suites. With LangSmith, we’ve seen a 43% performance increase over production systems, bolstering executive confidence to invest millions in new opportunities."
Varadarajan Srinivasan
VP of Data Science, AI & ML Engineering
"LangSmith has been instrumental in accelerating our AI adoption and enhancing our ability to identify and resolve issues that impact application reliability. With LangSmith, we can also create custom feedback loops, improving our AI application accuracy by 40% and reducing deployment time by 50%."
Padarn Wilson
Head of Engineering, ML Platform
"Before LangSmith, we didn't have a systematic way to improve the quality of our LLM applications. By integrating LangSmith into our application framework, we now have a cohesive approach to benchmark prompts and models for 200+ applications. This supports our data-driven culture at Grab and allows us to drive continuous refinement of our LLM-powered solutions."