Agentic RAG – the Path to more Accurate Data

Gartner predicts that by 2025, growth in 90% of enterprise deployments of generative AI will slow as costs exceed value, while more than 50% of companies that built large language models will abandon their efforts due to technical debt and complexity.

We are now seeing a huge push back by CIOs on GenAI which has left many organizations scrambling for alternative, more reliable solutions to remain competitive.

One solution that is gaining momentum is agentic Retrieval-Augmented Generation (RAG) – a method used to optimize the capabilities of large language models (LLMs) – with less costs and more accuracy, reducing the risk of potentially harmful hallucinations. This is particularly relevant given the results of a recent AI Trust Barometer survey commissioned by ABBYY which reported 32% of business leaders are worried about AI’s potential for hallucinations leading to inaccurate information.

Also Read: ABBYY Survey Reveals FOMO Drives AI Adoption in 60 Percent of Businesses, but Raises Trust Issues

Unlike traditional RAG systems which rely on singular queries to generate responses, agentic RAG can deal with more complex queries and better ensure the accuracy of retrieved information. It employs ‘intelligent agents’ that can cross-reference multiple sources, verify data, and use multi-step reasoning to ensure the output is both precise and contextually relevant – significantly lowering the chances of hallucinations.

However, there is one crucial ingredient for successfully implementing agentic RAG – good quality data. This is vital to ensure that the intelligent agents within agentic RAG can accurately retrieve and verify information. Poor-quality data, on the other hand, can lead to inaccurate or misleading results.

To mitigate this, users can deploy purpose-built AI platforms that can convert unstructured data and extract key data points, ensuring that the data fed into the system is of the highest quality.

For example, leveraging ABBYY’s purpose-built IDP platform helps streamline data preparation, retains perfect document structure, and provides key data points for 150 types of documents out of the box, making it the perfect tool for the RAG agents to have at their disposal. Agentic RAG can then be deployed to ensure that the retrieved information is both precise and comprehensive, which is ideal for complex queries.​

Also Read: Understanding Shadow AI: Key steps to Protect your Business

But how does this all translate to the real world?

In the healthcare sector, accuracy and reliability are paramount. For example,

Cancer treatment center LifeHouse used IDP to accelerate and improve access to critical patient information. Agentic RAG can be used in tandem to assist medical professionals by retrieving and verifying the information from multiple patient records, as well as medical journals, and even clinical trials. This ensures that doctors have access to the most up-to-date and accurate information, enabling them to make better-informed decisions.

Customer support teams can leverage agentic RAG to provide accurate and contextually relevant responses to customer queries. By cross-referencing multiple sources of information, the system can ensure that customers receive precise answers, enhancing their overall experience.

In the world of finance, agentic RAG can help analysts and advisors by sifting through vast amounts of financial data, news articles, and market reports. This allows them to provide clients with accurate and timely advice, improving investment strategies and risk management. No doubt more industries will benefit from Agentic RAG as we begin to see a wider range of use cases and applications – from legal research to content creation.

Future outlook of agentic RAG

As AI technology continues to evolve, we can expect to see even more advanced capabilities and applications for this powerful framework. Indeed, the ABBYY State of Intelligent Automation: AI Trust Barometer survey showed almost all business leaders (96%) plan to increase investment in AI next year, although only 84% expressed their trust in it. Tools that increase this confidence are therefore essential. Agentic RAG is now a game-changer in the world of AI and natural language processing (NLP). Its ability to retrieve, verify, and synthesize information with unparalleled accuracy and reliability makes it an invaluable tool for businesses and organizations across all industries. If you want to learn more about the state of AI with automation, I invite you to join our Intelligent Automation Month series of webinars during September.

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