Let's start with an understandable definition as it applies to what we have discussed so far. I will build on the definition as it applies to the use cases discussed in the previous blog post.
RAGs, in the context of AI, refers to "Retrieve-Augment-Generate" models. This framework combines three different AI techniques to enhance the performance and capabilities of natural language processing (NLP) models.
Retrieve: This involves retrieving relevant information from a large dataset or knowledge base. This can be accomplished using various information retrieval techniques, including keyword matching, semantic search, or other methods for extracting relevant data.
Augment: The augmentation phase involves enhancing the retrieved information with additional context or relevant data. This step aims to improve the overall understanding of the retrieved information and provide a more comprehensive context for subsequent processing.
Generate: This phase involves generating coherent and relevant text based on the retrieved and augmented information. The generation process often utilizes advanced natural language generation techniques, such as language modeling, text generation, or other methods to produce human-like text based on the input data.
RAGs models are widely used in various applications, including question-answering systems, chatbots, and content generation tasks, as they allow for a more comprehensive understanding of complex information and the generation of contextually relevant and coherent responses.
A great example of this can be used on both sides of the HR Hiring process. From the hiring side managers can use a basic job description with Generative AI to create a catered and professional post for LinkedIn Recruiting. Beyond that they can use an ATS to manged the influx of spammed application to whittle down the list and stack rank candidates. To counter that, candidates can use RAGs to input both the job description and their resume to cater a specific resume and cover letter to attempt to circumvent the back-end ATS blocker. there are many SaaS based services that offer both sides of this equation.
More to follow on the use cases, benefits and possible threats of RAGs.
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