reference string.Trajectory Evaluators: These are used to evaluate the entire trajectory of agent actions.Comparison Evaluators: These evaluators are designed to compare predictions from two runs on a common input.These evaluators can be used across various scenarios and can be applied to different chain and LLM implementations in the LangChain library.We also are working to share guides and cookbooks that demonstrate how to use these evaluators in real-world scenarios, such as:Chain Comparisons: This example uses a comparison evaluator to predict the preferred output. It reviews ways to measure confidence intervals to select statistically significant differences in aggregate preference scores across different models or prompts.Reference Docs‚ÄãFor detailed information on the available evaluators, including how to instantiate, configure, and customize them, check out the reference documentation directly.üóÉÔ∏è String Evaluators7 itemsüóÉÔ∏è Comparison Evaluators3 itemsüóÉÔ∏è Trajectory Evaluators2 itemsüóÉÔ∏è Examples1 itemsPreviousTemplate reposNextString EvaluatorsReference DocsCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.
Building applications with language models involves many moving parts. One of the most critical components is ensuring that the outcomes produced by your models are reliable and useful across a broad array of inputs, and that they work well with your application's other software components. Ensuring reliability usually boils down to some combination of application design, testing & evaluation, and runtime checks.
Building applications with language models involves many moving parts. One of the most critical components is ensuring that the outcomes produced by your models are reliable and useful across a broad array of inputs, and that they work well with your application's other software components. Ensuring reliability usually boils down to some combination of application design, testing & evaluation, and runtime checks. ->: reference string.Trajectory Evaluators: These are used to evaluate the entire trajectory of agent actions.Comparison Evaluators: These evaluators are designed to compare predictions from two runs on a common input.These evaluators can be used across various scenarios and can be applied to different chain and LLM implementations in the LangChain library.We also are working to share guides and cookbooks that demonstrate how to use these evaluators in real-world scenarios, such as:Chain Comparisons: This example uses a comparison evaluator to predict the preferred output. It reviews ways to measure confidence intervals to select statistically significant differences in aggregate preference scores across different models or prompts.Reference Docs‚ÄãFor detailed information on the available evaluators, including how to instantiate, configure, and customize them, check out the reference documentation directly.üóÉÔ∏è String Evaluators7 itemsüóÉÔ∏è Comparison Evaluators3 itemsüóÉÔ∏è Trajectory Evaluators2 itemsüóÉÔ∏è Examples1 itemsPreviousTemplate reposNextString EvaluatorsReference DocsCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.