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Coding in a Coffee Shop
How to Implement RAG: A Simple Walkthrough
Enhance your LLM-based Applications with LlamaIndex, LangChain, and Heroku
Having the correct data to support your use case is essential to a successful implementation of LLMs in any business. While most out-of-the-box LLMs are great at general tasks, they can struggle with specific business problems. They didn’t train on the data for your business problem, so they don’t have adequate context to solve it.
Businesses often have a treasure trove of internal data and documents that could meet this need for specific context. But, here’s the question: How do we integrate all this useful data (context) into the LLM without doing resource-intensive and time-consuming retraining or fine-tuning the LLM?
The answer is retrieval-augmented generation (RAG), a technique that enhances LLMs with just-in-time retrieval of close context information.
In this post, we’ll walk through how to use LlamaIndex and LangChain to implement the storage and retrieval of this contextual data for an LLM to use. We’ll solve a context-specific problem with RAG by using LlamaIndex, and then we’ll deploy our solution easily to Heroku.