Covering Disruptive Technology Powering Business in The Digital Age

Written by: Izzat Najmi Abdullah, Journalist, AOPG.

The world of Artificial Intelligence (AI) is rapidly evolving, but for many businesses, the barrier to entry can feel insurmountable. The need for specialised data science expertise and complex data integration often leaves smaller companies, or those without dedicated AI teams, on the sidelines. However, the rise of Retrieval-Augmented Generation (RAG) has levelled the playing field, making it a game changer for businesses of all sizes.

In a recent interview, Nick Brackney, Senior Consultant in Generative AI for Dell Technologies, shed light on how RAG is transforming AI accessibility and scalability for organisations, including those with limited in-house data science expertise.

Nick Brackney, Senior Consultant in Generative AI for Dell Technologies believes that RAG can make AI more accessible to others.

Breaking Down RAG: A Democratising Force in AI

Retrieval-augmented generation, commonly referred to as RAG, is a significant advancement in the field of AI. Nick explained that RAG offers a solution to a critical problem faced by many organisations: The need for data scientists to integrate data into AI models. Traditional generative AI models require extensive expertise to add and fine-tune data. However, RAG simplifies this process, making AI more accessible. “RAG is a massive democratiser,” Nick emphasised. “It allows companies without data scientists to leverage AI effectively.”

The approach involves using open-source models like LLaMA (Large Language Model Meta AI) or NVIDIA’s NeMo, which are then augmented with the company’s own data using RAG. This means that even organisations with a team of developers, but no specialised AI experts, can harness the power of generative AI. “You just grab a pre-trained model, use RAG, and you’re off and running,” he said. This ease of use is pivotal for companies of all sizes, enabling them to implement AI solutions quickly and efficiently.

Accelerating Innovation Across Industries

One of the standout features of RAG is its ability to enhance AI models by integrating domain-specific data. This capability is particularly beneficial for industries looking to leverage AI for innovation. The Senior Consultant in Generative AI at Dell Technologies highlighted the difference between fine-tuning and RAG: While fine-tuning changes how a model thinks, RAG changes what it knows by incorporating extensive datasets. “I can dump in all of my existing files, everything I’ve ever written, and now the model knows it,” he explained.

This integration allows the AI to apply the same logic and learning capabilities as an off-the-shelf model but with the added context of the specific data. This is crucial for industries that rely heavily on specialised knowledge. “It could be anything from sales and marketing materials to IT best practices, to even log files,” Nick noted. By embedding this rich context, AI models can provide more accurate and relevant insights, addressing challenges that generic models might miss.

Seamless Integration with Existing Solutions

Integrating RAG into Dell Technologies’ existing AI solutions is streamlined through Dell Technologies’ validated designs and reference architectures. Dell Technologies’ partnership with NVIDIA plays a crucial role here. The Dell Technologies senior figure elaborated on the different pathways available: Using Dell Technologies solutions with NVIDIA’s AI Enterprise for a fully integrated stack or customising open-source models for a more flexible approach. “We provide choice, whether a customer wants a fully integrated solution or to build their own,” he said.

This flexibility ensures that clients can choose the solution that best fits their needs. Nick further explained that for those seeking a ready-to-use, enterprise-grade AI solution, Dell Technologies’ partnership with NVIDIA offers a robust and reliable option. Meanwhile, clients preferring a tailored approach can leverage open-source models and Dell Technologies’ reference architectures to build their unique solutions.

Real-Time Decision-Making with RAG

Real-time decision-making is critical for many businesses, and RAG offers a distinct advantage in this area. Unlike fine-tuning, which cannot be real-time, RAG can integrate and react to data in real-time. “Fine-tuning can’t be real-time, but RAG can,” Nick pointed out. This capability is invaluable for applications that require immediate responses, such as monitoring factory sensors for potential issues.

He then described how RAG can be used at the edge, where real-time data integration is essential. For instance, a model trained to detect temperature anomalies in a factory can continuously receive and process data from sensors, enabling swift responses to any detected problems. “You’re feeding that data into the model, and now it’s seeing it and able to react,” he explained. This makes RAG particularly suited for environments where timely decision-making is crucial.

Staying Ahead in the Fast-Moving AI Landscape

To maintain a leading edge in the ever-evolving field of AI, continuous learning and adaptation are essential. Nick highlighted the importance of staying informed about advancements in AI technology. “I spend a ton of time social listening,” he said, referring to his method of staying updated through industry insights and social media.

One of the key takeaways from Nick’s approach is the emphasis on the quality and readiness of data as AI models continue to evolve. “Making sure your data is prepared, clean, and labelled is the biggest thing you could do as an organisation,” he advised. This preparation ensures that companies can seamlessly transition between different AI models as technology advances, leveraging the best available solutions without disruption.

Nick also highlighted the emergence of vector databases and other innovative approaches that are transforming how data is managed and utilised in AI models. These advancements underscore the importance of staying agile and informed in a field that is constantly moving forward.

“Everyone is going so fast,” he noted. “You have to be out there learning and listening.”

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