Hands-On LangChain for LLMs App: Answering Questions From Documents

In the previous articles of this series, we discussed how to load documents, split them, and then find documents related to a specific question. Now, let’s take the next step: using those documents and the original question to get an answer from a language model. 

This article will walk you through the process, explaining different ways to do question answering with the documents you’ve found. After sorting out storage and getting the right data, we’re now at the point where we use a language model to find the answers we need.

Table of Contents:

1. Overall Workflow for Retrieval Augmented Generation (RAG) 

2. Getting Started & Setting Environment Variables 

3. Prompt Development for RetrievalQA 

4. RetrievalQA with Map Reduce & Refined Chain

5. RetrievalQA Limitations 

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