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Large Language Models (LLMs) have limitations that can impact the product built on them — namely that they are:
Stateless (do not store or remember any information from previous inputs or outputs)
Hallucinations (they are limited to a dataset and can make up answers)
Implementing Retrieval-Augmented Generation (RAG) is a powerful way to avoid those limitations.
But what happens when they don’t work as you expect?
In this video, Edgar Palacios shares what a RAG application is and the limitations of LLMs, gives a brief overview of how to create a RAG application, and then purposefully causes it to break to show you how to troubleshoot some common issues.
You’ll see how to fix those mistakes and get insights in how to prevent them in the future.
Episode Outline:
00:24 – What is R.A.G. Application and What Does it Solve?
02:07 – Setting Up a R.A.G. Application
04:19 – Troubleshooting a R.A.G. Application
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