Evaluating RAG Pipelines

The effectiveness of a RAG pipeline is measured through four key evaluation benchmarks:

1. Context Relevance - It measures the relevance of the chunks retrieved by the auto_retriever in relation to the user's query.

2. Answer Relevance - It assesses the LLM's ability to generate useful and appropriate answers, reflecting its utility in practical scenarios.

3. Groundedness - It determines how well the language model's responses are related to the information retrieved by the auto_retriever, aiming to identify any hallucinated content.

4. Ground Truth - Measures the alignment between the LLM's response and a predefined correct answer provided by the user.

Additionally, BeyondLLM offers the RAG Triad method, which directly calculates all three key evaluation metrics mentioned above (Context Relevance, Answer Relevance, and Groundedness).

Each benchmark uses a scoring range from 0 to 10.

Upcoming Project

In the next module, we will create a project using BeyondLLM to build a RAG pipeline. This pipeline will extract information from a YouTube video and answer questions using retriever and generator components, utilizing open-source models from Hugging Face and Google's Gemini. Then we will also evaluate the response of the pipeline using the RAG Triad technique.