To RAG or not to RAG? That is the question...

Retrieval Augmented Generation (RAG)

Does RAG Still Matter? 

If you follow GenAi events and issues like we do, you cannot fail to have noticed the debate about whether to use Retrieval Augmented GenAI (RAG) or just use the public models (ChatGPT, Claude, Gemini etc.) directly? 

In recent years, the landscape of artificial intelligence has been significantly transformed by the advent of advanced language models. These models, such as GPT-4 and Anthropic 3.x, have demonstrated remarkable capabilities in understanding and generating human-like text, leading to a surge in their adoption across various industries. Two prominent approaches in this domain are Retrieval-Augmented Generation (RAG) and public Large Language Model (LLM) services. This article aims to contrast these two approaches and discuss the potential risks associated with the reduced availability of training data for public LLM services. 

Understanding RAG and Public LLM Services 

Retrieval-Augmented Generation (RAG) 

RAG combines the power of large language models with information retrieval techniques to enhance the quality and relevance of generated content. In this approach, the model is not solely reliant on its pre-trained knowledge but can actively retrieve and incorporate external information to generate responses. 

 

Public Large Language Model (LLM) Services 

Public LLM services, on the other hand, rely solely on the pre-trained knowledge encoded in the model during its training phase. These models, such as OpenAI's GPT-4, are trained on vast datasets encompassing diverse text from the internet, books, articles, and other sources. Once deployed, they generate responses based on their learned patterns and knowledge without actively retrieving external information. 

 

And Now the Reality…. 

The public model vendors will tell you that you should just use their models as they’ve been trained on mountains of data and can do everything. That was never really the case to begin with but, there’s a new trend that may make RAG even more important for companies that need accurate, contextual answers to their questions. 

The public LLM models were largely trained on free data that the LLM companies were able to scrape and harvest. More and more companies with public content sites are now starting to restrict scrapers and enforcing copyright protections to stop LLM model companies from using their data for free. Some companies are suing the LLM model companies for copyright violations and theft. If new data sources and access to existing data sources dries up, this presents several risks to public LLM services: 

1. Diminished Performance and Accuracy 

1. Bias and Fairness Issues 

1. Outdated Information 

1. Increased Dependency on Proprietary Data Sources 

The impact on end user companies will be that LLM data may not be as bountiful, as current or as accurate as you need for evolving information needs. Using RAG allows you to use your own institutional knowledge as a base and supplement that with public information as needed. Most of your internal content has been edited, vetted and can generally be trusted as genuine and accurate. The same can be said of professional subscription content providers such as research companies, industry associations and academic institutions.  

Conclusion 

Retrieval-Augmented Generation and public LLM services each offer unique advantages and face distinct challenges. RAG provides enhanced accuracy and relevance through real-time information retrieval, while public LLM services offer broad accessibility and rapid response generation. However, the reduced availability of training data poses significant risks to the performance, fairness, and adaptability of public LLM services. Addressing these risks requires concerted efforts to maintain open access to diverse and high-quality datasets, ensuring the continued advancement and equitable distribution of AI capabilities. 

One ways companies can mitigate this risk is build a RAG strategy around your own knowledge base and supplement that with external LLM services when required. 

About Evise.ai 

Evise.ai helps consultancies and advisory companies to harness the power of Generative AI with a turnkey platform, so that they can focus on reinventing their core business for relevance in the age of AI. Many consultancies and advisory companies are intrigued by the transformative impact of Generative AI, but often lack the talent and financial resources to create sustainable Generative AI strategies. This is where Evise.ai comes. We provide a purpose-built, turnkey Generative AI platform for sustainable business value realization. 

Visit us at www.evise.ai to find out how we can provide instant value realization at the most affordable price points.