Quickly Evaluate your RAG Without Manually Labeling Test Data

Quickly Evaluate your RAG Without Manually Labeling Test Data AI News, Ahmed Besbes, AI, AI tools, Innovation, itinai.com, LLM, t.me/itinai, Towards Data Science - Medium 🚀 **Automate RAG Evaluation for Improved Performance** Today's discussion is all about streamlining the evaluation process of your Retrieval Augment Generation (RAG) apps without the need for manual intervention. Assessing the performance of your RAG is crucial for its successful deployment in production. It provides vital quantitative feedback for refining and optimizing your systems, as well as meeting the expectations of clients and stakeholders. **Generating a Synthetic Test Set Automatically** When evaluating your RAG's performance, you require an evaluation dataset containing questions, ground truths, predicted answers, and relevant contexts utilized by the RAG. This can be achieved by generating questions and answers from the RAG data and running the RAG over these questions to make predictions. The process involves splitting the data into chunks, embedding it into a vector database, fetching similar contexts, and generating questions and answers using a prompted template. **Key RAG Metrics** Before diving into the technical details, it's essential to understand the four fundamental metrics used to evaluate RAG: Answer Relevancy, Faithfulness, Context Precision, and Answer Correctness. Each metric provides unique insights, and it's important to consider them collectively for a comprehensive evaluation of your application. **Leveraging Ragas for RAG Evaluation** Ragas is a powerful framework designed to assist in evaluating Retrieval Augmented Generation (RAG) pipelines. By configuring Ragas to utilize VertexAI LLMs and embeddings, you can easily compute the four key metrics for your RAG. This framework allows you to call the evaluate function on the synthetic dataset, specifying the metrics you want to compute. **Practical Solutions and Next Steps** Generating a synthetic dataset to evaluate your RAG is an effective initial step, especially when labeled data is not readily available. However, this approach also presents its own challenges. To address these issues, you can fine-tune your prompts, filter out irrelevant questions, create synthetic questions on specific topics, and utilize Ragas for dataset generation. 🔗 **Useful Links:** - AI Lab in Telegram @aiscrumbot – free consultation - Quickly Evaluate your RAG Without Manually Labeling Test Data - Towards Data Science – Medium - Twitter – @itinaicom Feel free to engage with the AI Lab in Telegram for further insights and assistance in implementing these practical solutions for your RAG evaluation needs. #AI #RAG #VertexAI #AIevaluation #RagasFramework