Addressing the Challenges of Generative AI in Industry by Embracing RAG

Addressing the Challenges of Generative AI in Industry by Embracing RAG
Addressing the Challenges of Generative AI in Industry by Embracing RAG

Large Language Models (LLMs) have revolutionized various industries with their ability to understand, interpret and generate human language. However, they also come with their own challenges, including producing inaccurate or misleading information (hallucinations), privacy concerns and security vulnerabilities. This article explores these challenges and delves into the innovative approach of Retrieval Augmented Generation (RAG) to overcome them, making LLMs even more powerful and reliable for critical industrial applications.
LLMs have access to vast amounts of textual data, but their training data may be outdated and drawn solely from the public domain. LLMs need access to an organization’s industrial data to make generative AI work for industry. By “training” LLMs on curated and relevant data, similar to how ChatGPT is trained, we can improve the reliability and accuracy of its responses for industrial applications.
To incorporate generative AI into a digital strategy, industrial organizations must have three fundamental pieces of  architecture in place:

Data contextualization

Contextualized data is crucial in ensuring LLMs deliver relevant and meaningful responses. For instance, providing data and documents related to those assets and their explicit and implicit semantic relationships becomes crucial when seeking information about operating industrial assets. This contextualization enables LLMs to comprehend the task and generate contextually appropriate answers.

Industrial knowledge graphs

Creating an industrial knowledge graph is necessary to improve data quality for LLMs. This graph processes data through normalization, scaling and augmentation to ensure accurate and trustworthy responses. The old adage of “Garbage-In → Garbage-Out” applies to generative AI, emphasizing the significance of enriching data to enhance LLM performance.

Retrieval Augmented Generation (RAG)

RAG is a cutting-edge design pattern enabling LLMs to utilize specific industrial data when responding to prompts directly. By incorporating in-context learning, RAG allows LLMs to reason based on private contextualized data, thereby providing deterministic answers instead of probabilistic responses based on existing public information. Additionally, RAG enables us to keep industrial data proprietary and secure within the corporate tenant. Like any advanced technology, LLMs can be vulnerable to adversarial attacks and data leakage. In an industrial setting, these concerns are magnified due to sensitive data, such as proprietary designs and customer information. Ensuring proper anonymization, safeguarding LLM infrastructure, securing data transfers and implementing robust authentication mechanisms are vital steps to mitigate cybersecurity risks and protect sensitive information. RAG allows for maintaining access controls, building trust with large enterprises and meeting stringent security and audit requirements.
By leveraging data contextualization, Industrial Knowledge Graphs and RAG technology within a generative AI solution, we not only address data leakage, trust and access control and hallucination challenges, but we can also impact the overall efficiency and cost of the solution.
LLMs have context window limitations, restricting the range of tokens they can consider when responding to prompts. Additionally, each token adds to the total cost of each query. If you think of these queries like Google Searches, you can see how easily cost might add up. To overcome this, contextualizing proprietary industrial data, creating an industrial knowledge graph, and optimizing queries via RAG becomes crucial. These steps ensure LLMs have access to a searchable and semantically meaningful source of inputs, thus leveraging vast industrial data more effectively.
In conclusion, while LLMs offer remarkable potential for various industries, addressing challenges such as inaccuracies, security vulnerabilities and privacy risks is crucial. By curating and contextualizing data, building industrial knowledge graphs and leveraging cutting-edge techniques like RAG, LLMs can become valuable assets in streamlining operations, automating tasks and deriving actionable insights for businesses in diverse sectors.

About The Author

As Cognite’s chief product officer, Moe Tanabian spearheads product strategy, execution, and management, driving innovation at scale in the realms of software products, AI, ML and IoT.
Before joining Cognite, Moe served as Vice President and General Manager of Azure Light Edge at Microsoft, where he led a $2 billion P&L for Microsoft’s IoT, OT and embedded industrial products. Prior to that, he held influential positions at Samsung, where he excelled as Vice President of Smart Products and IoT, and Amazon, where he played a key role in building and delivering the Amazon Android Appstore for Kindle Fire and Amazon Phone devices.
Moe’s expertise extends beyond his impressive leadership roles. He holds a master’s degree in Systems and Computer Engineering from Carleton University, Ottawa, Ontario, and an MBA from the School of Business at Queen’s University, Kingston, Ontario. With an outstanding track record of driving innovation and leveraging technology to shape the future of industries, Moe continues to be a driving force in the realm of cutting-edge technology and its applications.

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