Introduction

Introduction

LLMs and, by extension, RAGs, are still fairly new technologies with many rough edges. The potential is immense, as seen by the sharp rise in popularity of ChatGPT since its launch. RAGs, in particular, have shown enormous potential, as you’ve seen in the previous lessons.

However, some hesitate to adopt these technologies for one significant reason: accuracy. These apps are known to hallucinate or sometimes give inaccurate and irrelevant results. This can have dire consequences when used in critical environments such as healthcare, weather forecasting, or law. It’s essential to be able to evaluate your RAG, identify problem areas, and improve upon them.

By the end of this lesson, you will have learned to:

• Design evaluation metrics for RAG system performance.
• Implement techniques to measure and improve accuracy and relevance.
• Optimize RAG systems for specific use cases (e.g., chatbots, question-answering).

Continue to the next section to discover useful metrics that can guide you in optimizing your RAG app.