Data Connections and Generative AI

The world has witnessed a rapid evolution in the information technology sector. One significant trend is the rise of generative artificial intelligence (AI). This type of AI can create new content, such as text, music, images, and others. Essentially, the technology enables machines to ‘think’ like humans and perform tasks extremely fast. 

While generative AI signifies a tremendous milestone in Artificial Intelligence engineering, the foundation lies in data. Without data, there would be no AI since all types of AI, like machine learning models, predictive AI, and analytics AI, use data as fuel. 

In this resource, we explore the important relationship between data connections and generative AI. 

Utilizing data connections for real-time generative AI output

The solution to acquiring data real time includes establishing a pipeline process to receive streaming data, new documents and integration with webhooks to process updates as they occur.

The real-time generative AI output has extensive application in the current world. Most industries are using the technology to leverage their performance. A good example of an area where companies are putting real-time generative AI output into good use is in call centers.

The technology has improved customer support through the use of chatbots. The chatbots are trained to interact with customers where they can answer questions in a conversational manner. They can provide product information, answer questions, and generate surveys at the end of a conversation. 

This ability of bots to understand natural language and generate appropriate responses has enabled businesses to reduce the cost of customer care services. They have also reduced customer waiting time, thus improving the overall experience. 

While utilizing real-time data for generative AI is great, it’s important that companies understand the ethical concerns and potential biases involved. The views and ethics of the person doing the interpretation will have a lot of bearing on the outputs. Aspects such as correlation with other data can also be subject to bias. AI needs to be trustworthy or the value being offered is diminished

While engineers constantly strive to minimize bias, unintentional bias is common and poses a big challenge. The level of bias can be reduced by; 

• Maintaining high ethical standards 
• Utilizing feedback 
• Maintaining high-quality training data 
• Upholding quality assurance standards 

How data connections extend and augment generative models

In general, AI models are trained using the proprietary data of the organization. Much of this data is static and may include data such as product specifications, policies and procedures. 

Additionally, most companies have operational systems that drive their core business processes. Examples are ERP systems, CRM systems or specific line of business systems. The processes that are inherent in these systems are what operates the business.  Connecting to these operational systems makes it possible to correlate the current picture with other time periods and predict outcomes based on the correlation. Examples of common outputs include things like turnaround time, cycle time, error rate etc. 

The augmented generative model is being adopted in many industries, such as; 

• Demand prediction: The models are essential for creating product demand predictions based on historical data and current trends. 
• Supply chain management: You can enhance supply chain through route optimizations, shipping time prediction, and predicting potential disruptions. 
• Customer expectation settings: You can use generative models to create personalized customer expectations. 
• Process optimization: The models are used to identify problems and inefficiencies in end to end processes for example in manufacturing.  

How to optimize (train) data connections for generative AI

Since data connections involve massive amounts of data, optimization is key to ensure less training time and highly efficient models. It’s worth noting that data connection quality is more crucial than having a powerful algorithm. 

To optimize data connections, you need to follow the following actions. 

1. Organization and cleaning

The data that is collected from the various sources like documents and others requires to be organized and cleaned. Organization entails classifying data depending on subject, theme, or topic. This type of organization makes it easy to focus on specific topics and also gets rid of duplication. 

In cleaning, the unnecessary objects like texts and other characters are removed. The higher the level of data cleanliness, the better the results. 

We also recommend breaking the data into small clusters (tokens) in order to speed up the training process. 

2. Data augmentation 

Data augmentation is all about increasing the diversity of the training dataset through transformations. Some of the transformations that are normally applied include rotations, flips, zooms, and shifts.

The goal of data augmentation is to ensure that the model does not overfit to specific patterns. As a result, it can handle multiple real-world variations of data. 

3. Feature engineering 

Feature engineering entails selecting the right ingredients (features) and preparing them in a way that will help the generative models to learn and give better results.

It’s like baking a cake. The baker chooses the best flavors and prepares them in the right way like ensuring the right sizes . They will then mix the ingredients the right way. It’s the same thing with data connections. You want to make sure that the input is optimized.

The process can also involve data manipulation such as addition, deletion, mutation combination, and others to improve accuracy. 

It’s also important to keep monitoring and evaluating data connections to assess their impact on generative AI performance. You should have a baseline of test cases backed by fact from the data and ensure the responses meet the test case criteria and not “wonder” from the facts of the test case.

Challenges in data connections for generative AI 

Persistent connectivity and refreshing authentication are some of the common challenges you can expect to encounter when utilizing data connections for generative AI.  

1. Persistent connectivity

The connection between the AI model and the underlying data sources must be consistent. Maintaining this persistence is a challenge, and so any disruptions will lead to issues such as high latency and even model failure. Address this challenge with robust mechanisms for error handling. 

2. Refreshing authentication 

The permissions that allow the AI models to access data often have time limits. So, refreshing authentication is necessary to ensure that the AI keeps its “key” current to continue generating outputs.

Now, being able to orchestrate a seamless and timely process to obtain new access permissions before the existing ones expire is where the challenge lies.

Failure to have a robust system to manage and update security credentials will lead to lapses in authentication which will lead to disruptions in data access. This means the generative AI model will not be in a position to operate continuously.

3. Changes in the structure or meaning of the data 

This pertains to the dynamic nature of data sources. Remember the structure or meaning of data attributes evolve over time. As the changes occur, the AI model must adapt properly to the changes. This is necessary to maintain the accuracy of outputs.

The challenge lies in developing algorithms plus processes that are capable of adjusting dynamically to shifts in data attributes. 

Conclusion 

One of the greatest benefits of effective data connections is the ability to maintain a trustworthy go to resource for consistent, straight-forward fact based responses. Things change at such a great pace that LLM’s need to stay current with reality by continually updating the LLM with data feeds and data streams. This is where data connections come in to ensure that LLMs do not become stale and that they remain current and trustworthy. 

The other benefit is that by training an LLM on all the available data on a subject, it’s easy to uncover unrealized correlations and insights through a refining set of questions on a topic. 

Let’s talk about your operation and brainstorm the possible.