Chatting with ChatGPT is fun and informative — I’ve been chit-chatting with it for past time and exploring some new ideas to learn. But these are more casual use cases and the novelty can quickly wean off, especially when you realize that it can generate hallucinations.
How might we use it in a more productive way? With the recent release of the GPT 3.5 series API by OpenAI, we can do much more than just chit-chatting. One very productive use case for businesses and your personal use is QA (Question Answering) — you ask the bot in natural language about your own documents/data, and it can quickly answer you by retrieving info from the documents and generating a response [1]. You can use it for customer support, synthesizing user research, your personal knowledge management, and more!
In this article, I will explore how to build your own Q&A chatbot based on your own data, including why some approaches won’t work, and a step-by-step guide for building a document Q&A chatbot in an efficient way with llama-index and GPT API.