RAG for Real World Generative AI Applications

RAG for Real World Generative AI Applications


In the first part of our exploration into privacy-enabled retrieval-augmented generation (RAG) for enterprise generative AI, we delved into what RAG is, how it works, and its many benefits. We also addressed the crucial issue of data privacy and how confidential computing can secure RAG systems. In this second blog in the three-part series, we’ll dive deeper into the power and potential of RAG, backed by data points and real-world applications that highlight its transformative impact on enterprises.


RAG Advances Performance, Data Precision and Efficiency

Improved Response Accuracy

One of the standout benefits of privacy-enabled RAG is its ability to enhance the accuracy of responses generated by AI systems. By leveraging enterprise-specific datasets, without needing to omit sensitive information or IP, RAG systems significantly reduce the incidence of hallucinations and improve response accuracy. According to a recent study by AI researchers at MIT, RAG systems reduced incorrect answers by 35% compared to standard LLMs.

Cost and Resource Efficiency

RAG systems are also more cost effective. Training large models from scratch or even fine-tuning them with specialized data can be prohibitively expensive. Using large context windows, such as from Google Gemini, can also be cost and time intensive. By contrast, RAG systems utilize pre-trained models and enhance them with relevant data on the fly, which significantly lowers computational costs. This past February, Paul Walsh, Analytics and AI Director at Accenture, wrote a great piece, Managing the Cost of AI, where he talks specifically on how RAG pipelines can effectively lower both computational and latency costs. 

Versatile Applications Across Industries

  • Healthcare: In the healthcare sector, RAG systems that leverage confidential computing can be a game-changer. By integrating sensitive data like patient records, as well as medical literature and treatment guidelines into the RAG system, healthcare providers can offer more precise diagnostic and treatment recommendations. A great example of this is Apollo 24/7, one of the largest multi-channel digital healthcare networks that has partnered with Google to leverage RAG to complement their Clinical Intelligence Engine (CIE) with a promise to revolutionize healthcare.
  • Financial Services: For financial services, privacy-preserving RAG enables AI systems to navigate complex regulatory environments and provide tailored financial advice. By leveraging financial reports, regulatory documents, and market data, RAG systems help institutions stay compliant while offering personalized investment strategies. AI coupled with a RAG strategy in finance can extend beyond regulatory to enable improvement in productivity, data utility and precision, as well as advancing LLM efficacy through targeted ancillary data sources.
  • Customer Support: Customer service departments benefit immensely from RAG. By integrating product manuals, customer interaction logs, and FAQs, RAG-powered assistants can resolve customer queries more effectively. In their recent blog, “RAG — The Hottest 3 Letters in Generative AI Right Now”, Salesforce.com shared that one of their customers was able to improve their own ability to more quickly and efficiently complete cases, improving by 67% by leveraging RAG.

Future-Proofing Enterprise AI

Scalability and Flexibility

RAG systems are inherently flexible and scalable. As enterprise data grows, privacy-enabled RAG systems are built so that they can easily incorporate new datasets without the need for extensive retraining. This adaptability ensures that AI solutions remain relevant and effective over time. However, achieving scalable RAG with the meaningful flexibility is inherent to the use case, the business and most importantly a demonstrable RAG strategy.  An example of this can be found in research conducted by several PhDs at Cornell University, ERATTA: Extreme RAG for Table To Answers with Large Language Models. RAG simplification requires an education on how to best scale AI with RAG. 

Driving Innovation

By enabling secure and efficient use of specialized data, RAG systems with confidential computing foster innovation. Enterprises can develop new AI-driven products and services that were previously unattainable due to data privacy concerns. RAG should be seen as a business and innovation enabler versus a roadblock when it’s strategically adopted and rolled out. In a recent article titled, The RAG Effect: How AI is Becoming More Relevant and Accurate, Forbes Business Council member, Samder Khangarot calls out, “By proactively addressing these roadblocks and taking a strategic approach to implementation, leaders can successfully harness the power of RAG and drive innovation within their organizations”. 

Strengthened Security and Compliance

Regulatory Compliance: RAG systems, when coupled with confidential computing like Inpher’s SecurAI, deliver enhanced security and data precision solutions for regulatory compliance. These systems ensure that sensitive data is handled in accordance with privacy laws such as GDPR, CCPA, and HIPAA. A survey by the International Association of Privacy Professionals (IAPP) found that 72% of companies using RAG with confidential computing reported easier compliance with data protection regulations.

Mitigating Data Breaches: Confidential computing ensures that data remains protected during processing, reducing the risk of breaches. This is crucial as data breaches can have severe financial and reputational repercussions. A global study by MIT Technology Review Insights revealed that as many as 77% of participants see regulation, compliance, and data privacy as significant hurdles to the swift adoption of GenAI.



As enterprises increasingly rely on AI systems, managing the security posture of these systems becomes critical. Enterprises looking to harness the full potential of generative AI should explore implementing privacy-enabled RAG systems. Across a wide variety of industries, these systems enable enterprises to future proof their use of AI, by supporting scalability, innovation, compliance, and more.

With solutions like Inpher’s SecurAI, businesses can achieve meaningful and secure AI adoption, paving the way for a future where data-driven decisions are both reliable and safeguarded. For more detailed insights and to explore how Inpher’s SecurAI can transform your enterprise AI strategy, click here.