LLM Observability Deep Dive

Background

LLMs, or large language models, are a type of artificial intelligence (AI) that have been trained on massive datasets of text and code. This allows them to perform a wide range of tasks, including generating text, translating languages, writing different kinds of creative content, and answering your questions in an informative way.

Most of the LLMs are still under development, but they are already being used in a variety of applications, such as customer service chatbots, search engines, and creative writing tools. As LLMs become more sophisticated and widely used, it is increasingly important to be able to monitor and understand their behavior. The customer does not want to take LLMs as a black box but they want to know what is happening in this black box. This is where LLM observability comes in.

LLM observability is the practice of collecting and analyzing data about an LLM’s performance and behavior. This data can be used to improve the LLM’s performance, detect biases, diagnose issues, and ensure reliable and trustworthy AI outcomes.

Data Should be Observed

There are five main types of LLM observability data that we need to observe:

• Logs: Logs provide detailed information about the LLM’s input and output, such as the prompts that are given to the LLM and the responses that it generates.
• Metrics: Metrics are quantitative measures of the LLM’s performance, such as accuracy…