Evaluating a RAG Pipeline Using Ragas

What is RAG?

RAG (Retrieval-Augmented Generation) is a technique that combines information retrieval with a Large Language Model (LLM) instead of asking an LLM to answer a question only from pretrained knowledge, RAG first retrieves relevant information from specific knowledge sources, the retrieved information is then provided to the LLM as context, which helps the LLM generate a more relevant and grounded answer.

Typical RAG flow:

User Question → Retrieve Relevant Chunks → Provide Context to LLM → Generate Answer

Why Do We Need RAG Evaluation?

Building RAG pipeline does not automatically guarantee that it will produce correct answers. There can be problems at different stages of the pipeline.

For example:

Problem 1 – Poor Retrieval

The question may be:

“What is the work-from-home policy?”

But the retriever may return chunks about leave policy instead.

The LLM now has incorrect or irrelevant context.

Problem 2 – Missing Information

The correct...

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