How to Prepare Your Data for Generative AI

Generative AI trains on huge amounts of data to exhibit its remarkable capabilities. Consider OpenAI’s GPT-3, for example. This conversation AI model was trained on approximately 570 GB of text data! Similarly, DALL-E, their famous text-to-image gen AI, was also trained using massive datasets (over 400 million pairs of images) and 12 billion parameters.

That is a lot of data, and this average training data requirement is only increasing. As observed, visual and text-based training datasets have historically grown at 0.2 OOMs (orders of magnitude). However, this data isn’t simply collected and used as-is. It goes through an expansive process of aggregation, cleansing, standardization, labeling, and validation before it is used for training gen AI models.

In this article, we will explore a few tricks and techniques to prepare your data for generative AI. Read on to see what could happen with unprepared data, how to prepare it for optimal results, and what possible challenges you can encounter during the process.

Is your Data AI-Ready?

Before you start preparing and using your data for generative AI development, evaluate. Assess its quality, structure, and accessibility. Below is a list of questions that can help you with this evaluation.

• Do you have enough data to train a generative AI model?
• Is your data a reliable and trustworthy source?
• Is your data centralized and easily accessible for training your generative AI model?
• Do you have an enterprise knowledge graph or similar systems to provide context on relationships within your data?
• Have you established a data warehouse or lake house to manage and process large datasets efficiently?
• Is your data accurate and complete?
• Have you clearly defined what you aim to achieve with generative AI, including specific use cases and outcomes?
• Does your data cover a sufficient range of scenarios (breadth) and contain enough information (depth) for contextual insights?

This evaluation will help you understand the current state of your data and determine whether it’s ready for generative AI or requires further preparation.

Why Do you Need Data Preparation: Ensuring Data Quality for Generative AI

Generative AI outcomes rely on the quality of its training data. High-quality data is crucial for accurate learning, reduces biases, and generates reliable outputs. This happens through thoughtful data preparation that transforms raw, unstructured, or inconsistent data into a more actionable resource.

Here’s how data preparation improves quality:

• Clean, consistent, and accurate data minimizes noise and ambiguity, ensuring the model learns correctly.
• Quality data ensures the model isn’t overfitted to biased or skewed inputs, making it adaptable to diverse real-world applications.
• Well-prepared datasets make it easier to roll out continuous model updates and expansions without the need for extensive rework.
• High-quality datasets also streamline the AI model training process by optimizing resource and time utilization, which otherwise gets wasted with noisy, unprepared data.

What Happens When your Data isn’t Ready?

The need to prepare your data for generative AI stems from the famous “garbage in, garbage out (GIGO)” principle. So, skipping this process or undermining its veracity can have tangible effects.

Let’s look closely at why data quality for generative AI is indispensable and what can go wrong if it’s neglected:

• In customer services, a chatbot trained on poor, manipulated data can generate highly incorrect responses. You must have heard of a hilarious AI mishap with a Chevrolet dealership earlier this year. Their chatbot offered a Chevy Tahoe for sale at only US$1!

• Similarly, in industries such as finance & banking, poor data quality for generative AI assistants can result in faulty yet legally binding financial implications. The infamous Air Canada’s lying AI chatbot case is a classic example. One of their passengers sued the company, claiming faulty chatbot responses contradicting their bereavement fare policy, resulting in the company losing this case over neglected AI chatbot service.

• Such poor data quality implications are far worse in the healthcare industry. Inaccurate medical data can cause AI diagnostic tools to misinterpret conditions, suggest incorrect treatment recommendations, and risk patient safety. The same happened with IBM’s Watson, whose internal papers suggested that it incorrectly recommended cancer treatments, raising serious concerns globally.

Bad data can potentially derail even the greatest of generative AI systems!

Proven Techniques to Make your Data AI-Ready

Looking at the flip side—good, high-quality data can help generative AI systems reach unprecedented potential and open doors for more sophisticated use cases. The words of focus here are “good, high-quality data.” Below is an exhaustive process to prepare your data for generative AI.

1. Understand your Data

Before starting the actual process, take time to examine and understand your data. Knowing what kind of data, you have will help you plan the required collecting, cleaning, and processing techniques.

• You are dealing with structured data if it is largely organized in tables, like spreadsheets or databases. This data is often easier to analyze and process.
• If your data lacks a broader format and includes random text, images, videos, and audio files, it is unstructured. Preparing this data for generative AI will require specialized techniques like text tokenization, image normalization, etc.

2. Build a Data Lakehouse

As data is often a mix of both structured and unstructured data, you cannot rely on a single data aggregating and processing approach. In such situations, establishing a data lake house offers the flexibility (of data lakes) to store unstructured data while allowing you to enjoy the efficiency of structured data processing (of data warehouses).

This unified architecture works as a one-stop solution for storing, processing, and analyzing data, making it ready for generative AI without unnecessary complexity and operational overhead.

3. Clean and Organize your Data

Get started with data cleansing once you have a centralized repository. Here is how you can approach it:

• Remove Inconsistent Data Points and Noise: Discard duplicates, outliers, and irrelevant data points that can distort the training process. You can use Python data processing libraries such as pandas, OpenRefine, etc.
• Fill-in Missing Data: For numerical data, you can use central tendencies (mean, mode, or median) or predictive modeling techniques to fill in missing values. Alternatively, you can also remove rows/columns if there are too many data gaps.
• Add Missing Text Information: For text data, fill gaps by contextually imputing missing content or using pre-trained language models to estimate missing values.
• Enrich Visual Datasets: For image data, missing information might be addressed through techniques like inpainting.
• Standardize this Data: Regardless of the format, for accurate analysis and output, ensure uniformity in formats (e.g., consistent date formats or standardized units).
• Identify and Address Outliers: For generative AI, outliers can occur as unusually extreme examples in text (e.g., profanity or unrelated topics) or distorted/irrelevant images. Use context-aware techniques or anomaly detection tools to identify and either adjust or exclude these outliers.

4. Implement Advanced Data Augmentation with RAG

Even if you think you have enough data to prepare for generative AI, it’s nearly not enough, as generative AI requires unthinkable amounts of data to train on. More data means better generalization, fewer biases, and higher-quality outputs.

As one-off data collection efforts won’t suffice, you must augment more using advanced methods such as implementing RAG (retrieve, augment, and generate). These data pipelines ensure a steady flow of fresh and diverse data by continuously fetching relevant information from external sources, such as databases or APIs. Upon collection, they enrich this data using GANs (for images) or synthetic data to generate more datasets. Lastly, these pipelines feed the enriched dataset into the generative AI model for real-time learning and updates.

5. Engineer and Select Relevant Features

The next step in the data preparation process is emphasizing the most relevant aspects. This is often done through feature selection and engineering. In this process, AI/ML engineers create new features (or mold existing ones) to make data more meaningful for the generative AI model. For example, this could mean:

• Normalizing data using statistical methods like min-max, Z-score, etc. (for numerical data).
• Converting categorical text data (like “yes/no” or “red/blue/green”) into numerical formats the model can understand.
• Creating composite features like “price per unit” from existing data.

Once you have a set of features, identify the most impactful ones while eliminating irrelevant or redundant ones. You can do this by ranking them based on statistical relevance, testing subsets of features, or using algorithms like decision trees.

6. Label and Annotate Data

To ensure the chosen AI model can access and interpret your data, label and annotate to add context. Data labeling involves tagging data points with specific information to guide the model’s learning. For example, you can label spoken words, tones, or sound types if you have audio data. You can also use “positive/negative” as sentiment labels in social media data such as comments.

Similarly, annotation takes it further by involving more detailed markings to provide nuanced information about the data. You can use annotation tools or software to highlight certain text/words and draw bounding boxes to identify objects or timestamps within a clip.

Generative AI: a Beneficiary and a Driver of Data Preparation

Data preparation and AI have a symbiotic relationship. On the one hand, it requires volumes of high-quality training data; on the other, it helps prepare this training more efficiently and accurately. With AI-powered automation, you can simplify complex tasks with minimal manual effort and get access to AI-ready datasets faster.

• Data Cleansing and Noise Reduction: AI tools can detect inconsistencies, fill in missing values, and identify outliers automatically, ensuring cleaner datasets.
• Unstructured Data Processing: Generative AI frameworks with NLP and image recognition capabilities can help tokenize text, normalize images, and extract useful features from unstructured data.
• Advanced Data Augmentation: Technologies such as GANs can generate synthetic data to expand datasets, while RAG pipelines use AI to retrieve and enrich data in real-time.
• Feature Selection and Engineering: AI models can analyze datasets to automatically identify and rank impactful features, saving time and enhancing the efficiency of generative AI training.
• Annotation and Labeling: AI-assisted tools streamline annotation by automating repetitive tasks, while human-in-the-loop systems ensure accuracy for complex or nuanced data.

The Challenges in Preparing Data for Generative AI

Following the above techniques and steps to prepare your data for generative AI improves process efficiency and increases the likelihood of better results. However, implementing them requires proper planning and consideration. You also need a robust data infrastructure, subject knowledge, and expertise in big data management. And this is where the challenge lies.

1. Large Volumes of Data Without Losing Control: The staggering amount of data needed for generative AI often overwhelms systems and teams. There are storage limitations, pipeline bottlenecks, and data silos to work through.
2. Addressing Unstructured Data: Ensuring data quality for generative AI success depends on how you handle unstructured data, such as random text, images, etc. While it holds a lot of value, cleaning, organizing, and processing is quite challenging.
3. Optimizing Annotation and Labeling Processes: The varying nature and complexity of data make labeling and annotation challenging. For instance, image data may require object segmentation, audio data may need timestamp annotations, etc.
4. Maintaining Ethical Standards: Generative AI tools are often highlighted for their ethical limitations and skewed outputs. Additionally, they’re exposed to regulatory repercussions even if the data is mishandled slightly.
5. Balancing AI and Human Involvement: Preparing data for generative AI requires striking the right balance between process of data automation and human oversight. While tools can speed up tasks like labeling and cleansing, human input is essential for handling complex nuances and ensuring contextual accuracy. Too much reliance on one over the other can lead to errors or inefficiencies.

Breaking Through Generative AI Data Prep Barriers

You can overcome the above generative AI data preparation challenges with professional guidance. Here’s how:

• To handle volumes of complex data, you can either opt for cloud-based data lake houses or partner with some data management service provider. The latter is particularly beneficial for enterprise-scale data operations.
• To address the challenges in preparing unstructured data, you can outsource the process to any reliable data cleansing and processing service provider. Their established workflows and experience can help you save time and access AI-ready datasets without hassle.
• For nuanced and accurate data labeling, seeking professional data annotation services (for text, image, or video) is a strategic approach. These service providers combine AI-powered labeling tools with human oversight, ensuring accuracy and efficiency.

Working with professionals also guarantees compliance with data privacy and security regulations. They employ various tools and techniques, including AI-integrated ones, to minimize dataset bias. These professionals are also familiar with regional and global privacy laws, such as GDPR, CCPA, and COPPA (Children’s Online Privacy Protection Act). If required, they can anonymize sensitive data and apply masking techniques to protect individual identities. Additionally, every process step is meticulously documented to maintain transparency.

End Note

Generative AI utility is increasing every day, and so is the need for high-quality training data. As a result, it has become imperative to ensure the availability of AI-ready data—data that is well-structured, high-quality, and clean. This process to prepare your data generative AI for extends beyond merely collecting large datasets; it requires centralization, organization, and processing to get rid of inconsistencies or biases.

As generative AI models are inherently dependent on this data, the quality and structure play a crucial role in determining the accuracy and reliability of their outputs. With thoughtful and strategic data preparation involving proper data cleansing, organization, labeling, and ensuring consistent flow, your generative AI model will be far from delivering inaccurate responses.