Large Language models (LLMS) are capable of understanding, interpreting, and generating human language, revolutionizing all walks of life. However, they also face their own challenges, including the generation of inaccurate or misleading information (hallucinations), privacy concerns, and security vulnerabilities.
Large language models have access to large amounts of text data, but their training data may be outdated and only come from the public domain. Large language models need access to an enterprise's industrial data in order for generative artificial intelligence (AI) to work for industry. By "training" large language models on collated, relevant data, we can improve the reliability and accuracy of their responses in industrial applications.
To incorporate generative AI into a digital strategy, manufacturing companies can start with three basic architectures:
Data contextualization
Contextualizing data is critical to ensuring that large language models provide relevant and meaningful responses. For example, when seeking information about operating industrial assets, it becomes critical to provide data and documentation related to those assets and their explicit and implicit semantic relationships. This contextualization enables large language models to understand tasks and generate contextually appropriate answers.
Industrial knowledge map
Creating industrial knowledge maps is necessary to improve the data quality of large language models. This graph processes the data by normalization, scaling, and enhancement to ensure accurate and trusted responses. The old adage "garbage in → garbage out" also applies to generating AI, emphasizing the importance of enriching data to improve the performance of large language models.
Search enhancement generation
Retrieval Augmented Generation (RAG) is an advanced design pattern that enables large language models to leverage specific industry data in direct response to prompts. By incorporating contextual learning, RAG allows large language models to reason based on data from private contexts, providing deterministic answers rather than probabilistic responses based on existing public information.
In addition, RAG enables us to maintain the exclusivity and security of industrial data in the enterprise. Like any advanced technology, large language models can be vulnerable to adversarial attacks and data leaks. In an industrial environment, these issues require even more attention due to sensitive data such as proprietary designs and customer information.
Ensuring proper anonymization, protecting large language model infrastructure, ensuring data transfer security and implementing strong authentication mechanisms are important steps to reduce cybersecurity risks and protect sensitive information. RAG allows to maintain access control, build trust with large enterprises and meet stringent security and audit requirements.
By leveraging data contextualization, industrial knowledge graph, and RAG technologies in generative AI solutions, we can not only address challenges such as data leakage, trust and access control, and illusion, but also impact the overall efficiency and cost of the solution.
Large language models have context window restrictions that limit the range of tokens they can consider when responding to a prompt. In addition, each token increases the total cost of each query. If you think of these queries as Google searches, you can see how easy it is to add costs.
To solve this problem, contextualizing proprietary industrial data, creating industrial knowledge maps, and optimizing queries with RAG became critical. These steps ensure that lab managers have access to a searchable and semantically meaningful input source to make more efficient use of vast amounts of industrial data.
In conclusion, while large language models offer great potential for various industries, it is also critical to address challenges such as inaccuracies, security vulnerabilities, and privacy risks. By collating and contextualizing data, building industry knowledge maps, and leveraging cutting-edge technologies such as RAG, big language models can be a valuable asset in streamlining operations, automating tasks, and providing actionable insights for businesses across different industries.
