Mastering Evaluations in LangSmith: Enhancing LLM Performance

Author(s): Mostafa Ibrahim

Originally published on Towards AI.

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Large Language Models (LLMs) are AI models capable of generating text that resembles human language. They are trained on extensive text datasets and are suitable for various natural language processing tasks, including translation, question answering, and text generation.

Evaluating LLMs is necessary in order to verify their performance and the quality of the text they produce, which is particularly vital when their generated content is used for decision making or offering user information, which means that there is no room for error. But evaluating LLMs is not a one time action, it is a repetitive process that is done over time. Continuous assessment allows us to monitor their progress, identify areas of enhancement, and ensure the generated text remains accurate and of high quality.

This is where LangSmith comes in and plays a key role in simplifying and improving the evaluation of Large Language Models (LLMs) through its platform. It provides tools and features that streamline the assessment process, helping users efficiently analyze and compare model performance to ensure continuous improvement and reliability in their applications.

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According to LangSmith, evaluation refers to a process of assessing the performance of Large Language Models (LLMs) by examining the inputs and outputs of a… Read the full blog for free on Medium.

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Published via Towards AI