Mario is a senior principal software engineer at Red Hat working as Drools project lead. He has a huge experience as Java developer having been involved in (and often leading) many enterprise level projects in several industries ranging from media companies to the financial sector. Among his interests there are also functional programming and Domain Specific Languages. By leveraging these 2 passions he created the open source library lambdaj with the purposes of providing an internal Java DSL for manipulating collections and allowing a bit of functional programming in Java. He is also a Java Champion, the JUG Milano coordinator a frequent speaker and the co-author of "Modern Java in Action" published by Manning.
Checking the correctness of an application with an exhaustive suite of unit and integration tests is a natural task for any respectable software developer. Such a test suite also comes with other advantages like documenting the expected behavior of the application and enabling a fast feedback loop. This is all relatively straightforward when the components of your software are entirely deterministic, but how can you achieve something similar when a key part of it has a probabilistic nature?
This probabilistic nature makes it even more important to observe and collect real user inputs from production to better understand user needs and automate the evaluation of your LLM-infused application.
This talk will show in practice how to test an LLM-infused application with a mix of deterministic assertions and an LLM-as-a-judge approach. It will also demonstrate how LangChain4j 1.0 allows us to extensively observe the behavior of this application, and create a dataset out of the collected traces. Finally this dataset will be used in an evaluation framework through which assessing the performance of our RAG assisted LLM chatbot on both its retrieval and generation stages.
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