Using GenerativeAI with Your Own Information: A Deep Dive

In the progressing era of data-driven decision-making, it is essential for CDOs and CIOs to provide and process information in a targeted and efficient manner. Especially in the context of Generative Artificial Intelligence (AI), AI models are robust tools that support companies in unlocking the inherent value of large volumes of data. But how do leaders ensure that the correct information is delivered to the AI models, and what steps must be taken within the company to ensure the quality and relevance of this information?

The first essential step in the targeted provision of information to AI models is the careful preparation and provision of data. It is important that the data is not only clean and relevant, but also well-structured. Data preparation involves correcting errors, eliminating duplicates, and formatting the data in a way that is understandable and easily processable by the AI models.

Generative AI models can be efficiently used in this process by automating the conversion of unstructured to structured data. By using Generative AI, large amounts of unstructured data can be analyzed and converted into a structured form that is then easier to handle. After structuring, the models can be further used to create cheat sheets on various topics that provide a compact overview of essential information. These cheat sheets can then be reviewed and validated by human experts to ensure they are correct and useful. Once reviewed, these prepared and validated information can then be used to further train and improve the AI models.

The use of Generative AI in data preparation and provision not only contributes to increased efficiency but also allows for better scalability of the process, as large volumes of data can be processed faster and with less manual effort. By combining automated and manual steps, a higher quality and relevance of the provided information is ensured, which is crucial for the training and development of AI models.

In the world of generative Artificial Intelligence (AI), prompting and context play a central role in providing the models with the necessary information. These techniques enable efficient and effective generation of content that meets the specific requirements and context of a request.

Prompting

Context

The combination of prompting and contextualization allows for targeted control of information provision to AI models, significantly improving the quality and relevance of the generated content. These techniques are particularly beneficial in scenarios where complex, context-dependent responses are required, and contribute to increased efficiency and effectiveness of information processing in generative AI models. Through effective management of prompts and context, companies can optimize the performance of their AI models and ensure that the generated information meets requirements and expectations.

After the careful preparation and provision of data, another crucial step is supplementing knowledge through embeddings. Embeddings are powerful tools that capture the inherent knowledge in data and make it accessible to AI models. They serve as a bridge to close the gap between raw data and the knowledge usable for machine learning. By converting data into a vector-based representation, AI models can more efficiently recognize and utilize the semantic relationships and patterns in the data.

Embeddings enable a compact yet meaningful representation of knowledge. They are capable of capturing the essential features and relationships in data in a way that is easily accessible to AI models. This is particularly important in scenarios where the data is complex and high-dimensional, as embeddings help to reduce the dimensionality of the data without losing essential information.

Furthermore, embeddings allow for the integration of knowledge from different sources. For example, they can be used to incorporate external knowledge sources such as ontologies or external databases into AI models. By vectorizing these knowledge sources, they can be easily linked to the original data and utilized during the training and inference process.

The use of embeddings is therefore an effective way to expand the spectrum and depth of knowledge that AI models can access and contributes significantly to improving the performance and capabilities of the models. It is a fundamental technique to take AI models beyond mere processing of raw data and enable them to gain a deeper understanding and better interpretation of the underlying information.

Embeddings are a form of data representation where elements such as words, phrases, or even images and audio are transformed into vectors of real numbers in a high-dimensional space. This transformation facilitates the handling and processing of these elements by machine learning models. Each point in this vector space corresponds to a unique element, and the geometric relationship between these points reflects the semantic or relational similarity between the elements they represent.

Embeddings transform categorical, textual, or other non-numeric data into a continuous vector space. For example, word embeddings convert text into a numerical form where each word is represented as a vector. The process begins by assigning a unique identifier to each element. An algorithm is then used to translate these identifiers into vectors such that semantically similar elements are positioned close to each other in the vector space. This is achieved through techniques like dimensionality reduction, which preserves the relative distances between elements while representing them in a lower-dimensional space.

In generative AI, embeddings are crucial as they enable models to efficiently process and understand the data. For example, embeddings allow for the conversion of text data into numerical vectors, which can then be fed into AI models for various tasks such as text generation, translation, or sentiment analysis.

A notable feature of embeddings is that they can be pretrained on one task and used in another. This is particularly advantageous in scenarios where data is scarce or training a model from scratch is computationally intensive. For example, a generative model like GPT-4 could utilize embeddings that have been pretrained by another model to understand the semantic relationships between words or phrases, thereby improving its performance in generating coherent and contextually relevant text.

One of the challenges in using embeddings is determining the right dimensionality for the vector space. A higher dimensionality might capture more information but at the expense of increased computational resources. Conversely, a lower dimensionality might be computationally intensive but lose essential information.

Efficient storage and management of embeddings pose a challenge, especially when it comes to optimizing access to and use of these embeddings in AI models. This is where vertical databases come into play, providing a targeted solution for storing and accessing embeddings. Vertical databases are specialized databases that focus on specific domains or industries and offer in-depth information on specific topics.

The main advantage of vertical databases lies in their ability to store information in a highly structured and organized manner, facilitating efficient data querying and usage. In the context of embeddings, they can help efficiently organize and make accessible the semantic relationships and knowledge contained within the embeddings.

Products such as Elasticsearch, Amazon RDS, or specialized graph databases like Neo4j can be used to manage embeddings in vertical databases. These products offer robust mechanisms for indexing, querying, and managing large sets of embeddings and can be configured to meet the specific requirements of domains or projects.

For example, if a set of embeddings representing legal concepts is stored in a vertical database specialized in legal topics, they can be organized and retrieved in a way that facilitates efficient finding and interpreting specific legal information. This eases the integration of these embeddings into AI models for legal analyses and supports the generation of insights into legal issues.

Choosing the right vertical database and efficiently organizing the embeddings within it are crucial factors to ensure that AI models can effectively utilize the knowledge contained in the embeddings. By incorporating embeddings into vertical databases, a solid foundation is laid for efficiently utilizing and expanding the knowledge in AI models, ultimately improving the quality and capabilities of the AI models.

It is of great importance to understand that merely providing information, even if well-prepared and organized in a vertical database, is not sufficient to create effective AI models. Loading a law book into a vertical database doesn’t make it a legal advisor. AI models require an adequate training environment as well as precise guidance and contextualization to draw useful and relevant conclusions from the provided data.

The targeted provision of well-prepared information is only part of the overall process of maximizing the benefits of AI models. It is essential for companies to invest in the development of methods and systems that enable AI models to effectively utilize the provided information. This can lead to valuable insights that contribute to better decision-making and optimization of business processes, ultimately strengthening the company's competitive position.