How MLOps accelerates AI Model Deployment

RAG, acronym for Retrieval Augmented Generation, constitutes one of the most exciting developments of artificial intelligence. The technique allows combining Large Language Models (LLMs) with external knowledge bases in order to increase the accuracy and reliability of generated answers. In other words, it means grounding the generative AI model with facts and information that were not previously employed to train the model.


The RAG technique was first introduced in a groundbreaking late 2020 paper, “Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks“, authored by several researchers associated with Meta. In the conclusive remarks, the authors discuss the broader impact of their findings, writing “the fact that [the LLM] is more strongly grounded in real factual knowledge […] makes it ‘hallucinate’ less with generations that are more factual, and offers more control and interpretability”.

The mitigation of hallucinations is indeed one of the most significant benefits of RAG. Imprecise, incorrect or altogether fabricated answers sometimes creep in LLMs’ answers, resulting in “factuality” and “faithfulness” hallucinations, according to the taxonomy suggested here. The former concerns the generation of factually incorrect answers, while the latter refers to discrepancies between the user’s questions/instructions and generated answers.

The causes of LLM hallucinations are legion, including flaws in the data source, outdated information, and “inferior data utilization” (ibid.), thus making it difficult to address the roots of the problem. The occurrence is significant, ranging from the 2,5% hallucination rate of the best performing LLM, GPT 4 Turbo, to the 22,4% of Apple OpenELM-3B-Instruct, according to the Hallucination Leaderboard.

We are talking about a potential issue that undermines the accountability and effectiveness of LLMs, especially if we consider business and public sector applications. The consequences can be serious. In early 2024, Air Canada was held liable for the incorrect information provided by their website chatbot, which promised a user a discount that was not available for them.

RAG: 5 Business Use Cases

Considering the risks associated with hallucinations, it is no surprise that the RAG framework has been welcomed by companies and institutions wanting to increase the reliability of their AI projects. Let us explore 5 useful business use cases that demonstrate the potential of this technology:

1. Content Summarization and Production

RAG means retrieve and generate. So they excel in digesting documents – be them numbers, words, images, etc. – and summarizing them into an understandable paragraph or a series of bullet points. This could be useful for a top-level executive that needs the gist of complex financial analysis, or a coder who does not have the time to peruse lengthy software documentation. We can also think of more complex content generation tasks, in which different sources or knowledge bases can be combined to craft relevant blog posts or presentations. 

2. AI-Powered Chatbots

LLMs are great for conversational agents. Their ability to imitate human reasoning and expression is unprecedented, if compared with the chatbot options available a few years ago. Yet, the most common and commercially available LLMs have no clue about the actual things they should talk about when operating as customer care agents or personal assistants. This is why feeding them a database of commercial practices, products and policies can greatly improve the quality and relevancy of responses. Moreover, it is possible to have the RAG-powered chatbots adopt a precise tone of voice and deliver a consistent experience to users engaging with the company.

3. Training, Education and LMSs

RAGs can power highly effective educational tools that provide personalized explanations drawing on large corpora of texts. Where an always preferable human teacher is not available, an LLM-enabled surrogate provides reliable learning support whose factual accuracy is guaranteed by the quality of input data. RAGs can also simplify the creation of company training and testing materials, and enhance content research by analyzing and retrieving relevant information from multiple, multimodal sources (text, images, audio, video).

4. Code generation 

It is no news that LLMs can variously support developers (and non-technical people) in writing and checking code, based on natural language prompts. With RAGs, we can further enhance the process by grounding the answer on already existing code, comments, or documentation – this can be particularly helpful to expedite repetitive tasks or boilerplate code writing. AI can also assist in the compilation of comments, analyzing code and the surrounding codebase to provide specific explanations.

5. Market Research and Sentiment Analysis

In the past, these tasks could be accomplished with ad-hoc AI solutions that necessarily required a lengthy development by data and technical teams. These in fact had to collect and prepare data, train and deploy the model, connect a dashboarding solution and much more, with significant costs and time-to-value. Today, RAGs dramatically accelerate the development of LLM applications that can be used to analyze reviews and social media content, providing valuable insights into customer experience and market trends.

Radicalbit's RAG Interface

Things can get even quicker with ready-to-use solutions. The Radicalbit platform, in this regard, offers a low code interface that allows non-technical users to create RAG applications in mere seconds. It supports the upload of different knowledge bases such as spreadsheets, Notion or PDF documents that can be combined with industry standard  LLMs such as Llama2 or 3, Mixtral or GPT. Click here to create your free account and start experimenting with RAGs right away!

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