Unveiling a Novel Approach: Harnessing the Power of Large Language Models for Contextual Question-Answering.

This guide shows how to use a different route by focusing on Large Language Models (LLMs) rather than creating a version of ChatGPT or relying on its training techniques. The goal is to leverage LLMs to grasp text representations and construct a straightforward question-answering interface that solely depends on the provided prior information.

Imagine it as crafting a rudimentary semantic search engine accompanied by a chat-like interface.

Now, you may wonder about the significance and potential applications of such an endeavour. While the allure of an all-knowing, general-purpose answering machine is undeniable, there lies greater value in developing an expert agent that provides finer control over generated responses and minimises the chances of errors.

This specialised agent can prove invaluable when it comes to extracting meaningful insights from extensive collections of data, all through the natural flow of conversation. It eliminates the arduous task of manually sifting through information, enabling a more efficient and seamless experience.

For instance: It can be used for unlocking answers from software documentation - imagine effortlessly retrieving specific information or troubleshooting tips from vast software documentation repositories, all through interactive conversational queries.

By utilising LLMs and implementing a tailored question-answering framework, it's possible to bridge the gap between data and insights, transforming how we interact with information.