Automated RAG Accuracy Evaluation

Issues in using RAG

There are many challenges in evaluating RAG systems. Objectively measuring the accuracy and relevance of information is complex and has many context-dependent factors, so simple indicators alone are not sufficient for evaluation. It is also difficult to set appropriate evaluation criteria for different use cases and requirements. In addition, preparing a large number of evaluation datasets (question-answer pairs) is also costly.

Automatic evaluation

An automated evaluation system is a method for measuring the performance of RAG efficiently and consistently. It allows rapid evaluation against a large number of test cases and enables continuous quality monitoring. Furthermore, evaluation datasets can be automatically generated using language models. Automated evaluation serves as a complement to manual evaluation and contributes to shortening development cycles.

tool:

For automated evaluation, we use an automated evaluation framework for RAG systems, such as Ragas, which automatically measures key metrics such as faithfulness, answer relevance, context precision, and context recall, allowing us to analyze multiple facets of the system's performance and identify areas for improvement.

Benefits of introducing automatic accuracy evaluation of RAG:

The main advantages of automatic evaluation are the efficiency and consistency of the evaluation. Compared to manual evaluation, it can process large amounts of data in a short time, and the evaluation criteria are unified, making it easy to compare results. It also enables continuous monitoring, allowing changes in system performance to be detected quickly. It also contributes to accelerating the development cycle. This evaluation can determine what language model to use, what sentence embedding model to use, what sentence segmentation size is best, etc.

Disadvantages of automatic RAG accuracy assessment:

Automated rating systems also have limitations. They cannot match human judgment, particularly when it comes to understanding context and subtle nuances. They also have difficulty dealing with unexpected edge cases, and their rating metrics may not fully reflect the actual user experience. Furthermore, they may require specialized knowledge to set and adjust rating criteria.

Service Details

This is a service that supports you in automating RAG accuracy evaluation within a month.

Specifically, we use NVIDIA NIM to generate synthetic question and answer data from internal documents, and use Ragas to automate accuracy evaluation for domain-specific tasks.

This program allows you to learn how to efficiently implement automation of RAG accuracy evaluation through sample code in Jupyter Notebook format, Q&A via email/chat, regular meetings, and lectures on how to use NVIDIA's NIM.