LlamaIndex: How To Evaluate Your RAG (Retrieval Augmented Generation) Applications
If you’ve followed my previous articles or spent some time on the internet, I’m sure you will find that it is super easy to build an LLM application. From PoC to production-ready, even no-code, low-code solutions can help to integrate the LLM app with the backend and automate most of the boring stuff, such as sending emails or triggering downstream jobs.
However, the big question in the table here is how you can ensure that your app is not answering with made-up facts and unknown material, or in jargon terms, hallucinations.
This article will explore how to evaluate your LLM app with an end-to-end process with LlamaIndex.
We can not just sit down, write code, and kaboom — here is your LLM app. We must evaluate the pipeline to make sure the content generated satisfies us, and if not, then identifying the area can be improved.
LlamaIndex already has fascinating posts on official documents with different evaluating/observing tools. I will leave more links in the references. In this article, I want to dig into the fundamentals of evaluating processes and creating our own. Access to those tools is great and very helpful in speeding up the evaluation process. However, as the old ones say:
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