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How to Train a Chatbot Using RAG and Custom Data
Retrieval-Augmented Generation made easy with Llama
What is RAG?
RAG, which stands for Retrieval-Augmented Generation, describes a process by which an LLM (Large Language Model) can be optimized by training it to pull from a more specific, smaller knowledge base rather than its huge original base. Typically, LLMs like ChatGPT are trained on the entire internet (billions of data points). This means they are prone to small errors and hallucinations.
Here is an example of a situation where RAG could be used and be helpful:
I want to build a US state tour guide chat bot, which contains general information about US states, such as their capitals, populations, and main tourist attractions. To do this, I can download Wikipedia pages of these US states and train my LLM using text from these specific pages.
Creating your RAG LLM
One of the most popular tools for building RAG systems is LlamaIndex, which:
- Simplifies the integration between LLMs and external data sources
- Allows developers to structure, index, and query their data in a way that is optimized for LLM consumption
- Works with many types of data, such as…
