Evaluating LLM Applications Using LangChain
Last Updated on June 11, 2024 by Editorial Team
Author(s): Youssef Hosni
Originally published on Towards AI.
Hands-On LangChain for LLM Application Development
When constructing a sophisticated application employing an LLM, a crucial yet challenging aspect revolves around evaluating its performance. How can you ascertain if it meets accuracy standards?
Moreover, if you opt to alter your implementation — perhaps by substituting a different LLM or adjusting the strategy for utilizing a vector database or other retrieval mechanisms — how can you gauge whether these changes enhance or detract from the application?
This article discusses the challenges of evaluating the performance of applications built with large language models (LLMs) and explores strategies for effectively assessing their accuracy and effectiveness.
It emphasizes the importance of understanding the inputs and outputs of each step in the application’s workflow and introduces frameworks and tools designed to aid in evaluation.
Additionally, it explores the concept of using language models and chains themselves to evaluate other models and applications. With the rise of prompt-based development and the growing reliance on LLMs, the process of evaluating application workflows is undergoing reevaluation.
Setting Up Working EnvironmentManual Evaluation & DebuggingLLM-Assisted EvaluationObserving Behind the Scenes
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