Now that we understand how large language models (LLMs) integrate personalized information and content, what are the critical aspects of monitoring and assessing them? Observability is crucial for understanding the functionalities and performance of large language models, especially in real-time use.
How can we ensure transparency, efficiency, and reliability in LLM systems? Discover the “Data Talks: Mastering Knowledge Processing and System Observability” event right here *video embedded* – featuring AI engineer Fernando Peres, who has over 25 years of experience developing solutions for various industries. He provides insights on optimizing LLM system performance in complex environments to improve accountability in decision-making processes.
“If you cannot measure it, you cannot improve it.”
How Does Observability Work?
LLMs are complex systems and without proper observability, it becomes difficult to understand their internal dynamics, leading to inefficiencies. So how do they operate? There are five pillars of large language model observability:
Evaluation assesses the outputs.
Traces and spans identify problems.
Prompt engineering tests different versions and options to observe what works in a specific context.
Search and retrieval allows access to the knowledge base, which can be evaluated for inefficiencies.
Fine-tuning finds and exports example data.
Key Concepts and Definitions
The evaluation playground consists of elements that help with its analysis and assessment:
A project is a box that enables you to organize everything related to a specific context for analysis.
A trace is a collection of runs that generate the final output.
Runs are individual components of a trace that can be analyzed separately.
Datasets are a series of questions and answers.
The Evaluation Playground
There are two stages where the evaluation takes place: pre-production (offline) and production (online).
Pre-production, when the application is offline, involves testing the application before going live. This stage, also known as the ‘ground truth’, checks whether the LLM solution is working efficiently, which requires quality data collection and annotation.
Production happens when the application is online, and undergoes continuous evaluation to detect any issues or problems. Which allows for ongoing monitoring of the LLM’s behavior in production.
The concept of observability and the importance of datasets is to evaluate whether the large language model application is answering questions correctly. The prompt functions through analyzing questions to determine the question's value.
The purpose of playgrounds is to test, with the aim of understanding how LLM systems function in order to ensure the transparency, reliability, and efficiency of these AI models.
Are you ready to jump into tech? Check out other articles by Ironhack that highlight the impact of AI on the tech industry.
