A Beacon of Innovation: What is Retrieval Augmented Generation?

Retrieval augmented generation (RAG) is being heralded as the “next big thing” in artificial intelligence. In a nutshell, RAG is a method of improving responses from generative AI by dynamically fetching additional knowledge from relevant outside sources.

Its two-step process works by providing access to a defined universe of knowledge outside of a large language model’s (LLM) training data, retrieving it and generating meaningful answers by leveraging external documents fed into it.

RAG addresses a key challenge; while LLMs generate human-like responses, their knowledge is static. RAG transforms them into dynamic systems by enabling access to an ever-updating information library.

Currently, beyond an enterprise building or fine-tuning an LLM at some expense, RAG is the recommended approach for leveraging generative AI for enterprises. Using RAG can help reduce errors in the content generated by AI and make sure responses are contextually aware and up to date. It can reduce AI hallucinations – where an answer provided by AI is incorrect – by grounding it in information that actually exists. 

The technology has a variety of uses in a business. For example, it can retrieve financial documents and information to summarise the key areas and generate reports based on accurate data, or verify facts and numbers by retrieving data from existing sources.

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It is possible to integrate RAG with almost any other complementary AI technologies to improve outcomes. It can integrate with natural language processing (NLP) tools, for example, to improve the quality and relevance of responses, or with intelligent document processing (IDP) solutions to improve the conversion of unstructured content and delivery of highly accurate and consistent data.

It has become passé to say now, but that doesn’t make it any less true that high-quality data is integral to the successful running of a business. Being able to quickly and accurately access information from vast databases and document sets can not only make sure outputs are reliable but also help manage the costs associated with large-scale projects.

While it is a useful mechanism for reducing hallucinations, it’s important to remember that RAG is not the be-all and end-all solution to the problems with generative AI. The core technology used in RAG is still word prediction, which is the same as most generative AI LLMs. This is based on identifying patterns, so technicalities in language mean it can still be susceptible to subtle errors in wording, even if the data itself is correct. 

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It’s important to take into account that any AI or tool used to augment AI is prone to some error and human input to verify and double-check the information is still recommended. Ongoing monitoring, testing and evaluation can help ensure RAG maintains its performance over time and adapts to new data or changes in user needs.

Balancing the risks of inaccuracies with the opportunities will be important for reaching RAG’s full potential. RAG systems will keep evolving and we will likely see better integration of real-time data sources, improved retrieval algorithms and enhanced data processing capabilities very soon.