Everything You Need to Know About LLMs Observability and LangSmith

Author(s): Adipta Martulandi

Originally published on Towards AI.

Why LLMs observability is important in your LLMs applications

This member-only story is on us. Upgrade to access all of Medium.

In the era of AI-driven applications, Large Language Models (LLMs) have become needs in solving complex problems, from generating natural language to assisting decision-making processes. However, the increasing complexity and unpredictability of these models make it challenging to monitor and understand their behavior effectively. This is where observability becomes crucial in LLM applications.

Observability is the practice of understanding a system’s internal state by analyzing its outputs and metrics. For LLM applications, it ensures that the models are functioning as intended, provides insights into errors or biases, shows cost consumption, and helps optimize performance for real-world scenarios.

As the reliance on LLMs grows, so does the need for robust tools to observe and debug their operations. Enter LangSmith, a powerful product from LangChain designed specifically to enhance the observability of LLM-based applications. LangSmith provides developers with the tools to monitor, evaluate, and analyze their LLM pipelines, ensuring reliability and performance throughout the lifecycle of their AI solutions.

This article explores the importance of observability in LLM applications and how LangSmith empowers developers to gain better control over their AI workflows, paving the way for building more… Read the full blog for free on Medium.

Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor.

Published via Towards AI