I automated content generation for my newsletter with R.A.G pipelines.
R.A.G standing for Retrieval Augmented Generation, is a prompting technique, that can be used to solve complex domain specific and knowledge-intensive tasks, using various general purpose language models.
This allows us to pass some data to the model, give some background knowledge and prompt(s). This enables factual consistency, improves reliability of the generated responses.
RAGs takes an input and retrieves a set of relevant / supporting documents given a source (eg. the api for my news platform).
Introduction
In this blog, I will be showing you how to create a pipeline for automated content generation using a R.A.G system and the opportunities that are available by creating such pipelines and also how I am making money with R.A.Gs (i guess you can say this is a RAGs to riches article). At the end of this article you would leave with.
- Understand what Retrieval Augmented Generation (RAGs) are.
- The building blocks for creating scalable and reliable RAGs.
- How to distribute content generated using RAGs and how to monetize the said content.
