Synthetic Data Generation with LLMs

In recent work with financial firms, the significance of Generative AI, especially Retrieval-Augmented Generation (RAG), has become evident. RAG acts as a crucial capability in LLM-driven applications, effectively linking document retrieval with response synthesis. This dual approach facilitates knowledge access in areas like customer support and research. Crucially, establishing clear evaluation criteria based on Test-Driven Development principles is vital to ensure the reliability of these AI systems. Developing evaluation datasets remains challenging, often requiring manual input from subject matter experts, which can be both time-consuming and costly.

Creating evaluation datasets for RAG systems has traditionally faced challenges, relying on subject matter experts to manually review documents and generate Q&A pairs, making it time-intensive and costly.

Defining clear evaluation criteria is key to ensuring LLM solutions meet performance standards, much like Test-Driven Development ensures reliability in traditional software.

Read at towardsdatascience.com