In the rapidly changing world of artificial intelligence, it has evolved into much more than mere predictions based on data analysis. It now emerges with unlimited potential for creative content creation and problem-solving models. With generative AI models like ChatGPT, chatbots represent improvements in language recognition capabilities. According to a market research report, the global generative artificial intelligence market is poised for exponential growth, expected to grow from USD 8.65 billion in 2022 to USD 188.62 billion by 2032, at a staggering CAGR of 36 ,10% during the forecast period 2023-2032. The dominance of the North American region in the market in 2022 highlights the widespread adoption and recognition of the potential of Generative AI.
Why is RAG important?
Every industry hopes to develop an AI implementation, such as Generative AI, that can leverage big data to deliver meaningful insights and solutions or provide greater customization and automation to harness the potential of AI. However, Generative AI by leveraging neural network and large language model (LLM) architectures helps companies improve while limiting content production or analysis that may be factually incorrect given the volume of data fed into the developed model, also known as “hallucinations” or providing outdated information.
To overcome this limitation, the augmented retrieval generation approach in LLM changes the way information or data is retrieved from other knowledge sources beyond the coded data or dated knowledge base. Therefore, RAG works in two phases – retrieval and generation – and, when combined with generative in LLM, provides more informed and relevant results to a user’s query or question. Long-form question answering (LFQA) is just one type of RAG that has shown huge potential in LLM models.
RAG is also an efficient and cost-effective approach since companies can save time and money retrieving relevant information instead of feeding all available data into language models and adapting the algorithm to a pre-trained model.
RAG use cases are spread across industries such as retail, healthcare, etc. RAG access to enterprise data is useful for customer-facing businesses. Therefore, businesses require their LLM models to deliver more relevant and accurate information with RAG. A selection of tools that offer RAG implementation with domain expertise. This approach further ensures reliability of results to its users by providing insight into the sources of AI-generated responses. Direct source citations allow for quick fact-checking. This further provides greater flexibility and control to LLM developers in validating and troubleshooting model inaccuracies as needed. Flexibility also extends to allowing developers to restrict or hide the retrieval of sensitive information to different authorization levels to comply with regulation.
Implementation of the RAG framework
Frameworks offered by tools such as Haystack can help build, test, and fine-tune data-driven LLM systems. Such frameworks help companies gather stakeholder feedback, develop guidelines, interpret various performance metrics, formulate search queries for outsourced searches, etc. Haystack offers companies the ability to develop models using the latest architectures, including RAG to generate better meaningful insights and support a wide range of use cases modern LLM models.
The K2view RAG tool can help data professionals derive credible results from an organization’s internal information and data. K2View powers RAG’s patented approach to Data Products, which are data assets for core business entities (customers, loans, products, etc.) that combine data to help companies better customize services or identify suspicious activity on a customer’s account. Reliable data products feed real-time data into the RAG framework to integrate the service user and provide relevant results by suggesting relevant instructions and recommendations. These insights are made available to LLM systems along with the query to provide a more accurate and personalized response.
RAG workflows offered by Nanonets are also available for companies to achieve customization based on company data. These workflows using NLP enable real-time data synchronization between different data sources and provide the ability for LLM models to read and perform actions on external applications. Daily business operations such as customer support, inventory management or marketing campaigns can be successfully managed through RAG’s unified workflows.
According to McKinsey, approximately 75 percent of the potential value created by generative artificial intelligence is focused on four key sectors: customer service, marketing and sales, software development, and research and development.
These platforms leverage expertise to effectively address implementation challenges, ensuring scalability and compliance with data protection laws. Moreover, designed RAG systems adapt to evolving business needs, enabling organizations to remain agile and competitive in dynamic market environments.
The future of RAG
As artificial intelligence continues to evolve, the integration of the RAG framework represents a key advance in improving the capabilities of Generative AI models. By combining the power of machine learning with the breadth of external knowledge sources, RAG ensures the reliability and relevance of answers generated by artificial intelligence and gives developers greater flexibility and control in refining and solving model problems. As companies struggle to rely on the accuracy of AI-generated responses as insights or answers to business questions, RAG is poised to revolutionize the landscape of AI-driven innovation, improved decision-making and enhanced customer experience.