How to Create Custom Datasets for AI and LLM Training

Every successful AI model—from GPT to Gemini to Claude—has one thing in common: it was built on data. Not just any data, but high-quality data that was cleaned, structured, and made training-ready.

Yet for most people entering AI development, building datasets is one of the most overlooked parts of the process. While AI tools get all the spotlight and architectures get the credit, it’s the dataset that does the real work.

In fact, a model’s performance depends on the quality of its training data. Even the best architecture won’t save a model trained on poor or generic data. That’s why companies building domain-specific AI need high-quality, well-annotated datasets.

This article explains how custom AI and LLM training datasets are created, covering key steps from data preparation and labeling to improving coverage and maintaining data quality. Throughout, we’ll use DepositPhotos’ multimodal AI training dataset as a practical example of how these principles work in the real world.

TL;DR

• Raw data must be cleaned, structured, labeled, and validated before training.
• Custom datasets should be balanced, diverse, and well-annotated.
• Off-the-shelf AI training datasets, such as DepositPhotos’ multimodal datasets, provide a reliable alternative to building from scratch.
• High-quality training data leads to more accurate and reliable models.

Understanding data quality and its role in AI training

Data quality, as defined by IBM, describes how well data fits its purpose across the AI lifecycle. It is typically measured by criteria such as accuracy, completeness, and reliability.

How dataset quality impacts model performance

An AI model has a limited understanding of the world beyond what its training data teaches it. Unlike a human who can ask questions, use common sense, or recognize when something seems off, a model relies on the data it was trained on. Its behavior is shaped by the patterns and information contained in that dataset.

When training data includes errors, duplicates, inconsistencies, or bias, the model learns the wrong patterns and produces unreliable outputs. These issues can lead to several common consequences: 

• Hallucinations: Cases where a model confidently generates content that isn’t true or real, including text or images. This happens when the AI invents new information that is not supported by facts.
• Factual errors: Cases where a model produces information that does not match real or verified facts. This occurs when the AI provides incorrect details about facts.
• Benchmark contamination: Cases when an AI model is trained on test questions or answers that are supposed to be only for evaluation.

In contrast, models trained on high-quality licensed data offer several key advantages:

• Faster training, as well-structured data requires less processing
• Higher accuracy, as the model learns correct patterns
• Fewer hallucinations, as clean datasets reduce the likelihood of generating unsupported outputs
• Reduced bias, as carefully curated datasets minimize skewed learning
• Better generalization, as consistent data improves performance on unseen inputs
• Commercial safety, as data comes from an authorized source
• Lower maintenance costs, as fewer post-deployment fixes are required

Investing in clean, well-annotated training data upfront is always more efficient than fixing a poorly performing model after deployment.

Companies that lack the resources to collect and process data in-house often turn to licensed providers. For example, DepositPhotos offers access to over 330 million multimedia assets enriched with metadata, helping organizations accelerate AI development while maintaining data quality and compliance.

Quick answer: What makes AI training data high-quality?

AI or LLM training data is considered high-quality when it is accurate, relevant to the task, consistent, complete, reliable, and valid, with minimal errors, duplication, bias, or noise.

From raw data to training-ready data

Since an AI model learns from everything it is given, feeding it raw data is not an option. Any input material must be cleaned, structured, and prepared before training can begin.

Why raw data needs preparation before use

Raw data refers to information in its original, unprocessed state. While it preserves full detail, it often contains errors, duplicates, missing values, irrelevant content, and inconsistent formatting. It also tends to be unevenly distributed across categories, leading to dataset imbalance.

Without data preparation, these issues degrade model performance. The model may learn incorrect patterns, struggle to generalize to new data, and produce biased or unreliable results. In some cases, low-quality data can also significantly increase training time.

Processing raw data means cleaning, organizing, and standardizing it into a format that an AI model can effectively learn from. This step ensures that the model receives clean, consistent, and relevant data, giving it the best possible foundation for learning.

Quick answer: What is dataset imbalance?

Dataset imbalance occurs when one class dominates the dataset relative to the others. For example, a phishing email detection dataset where 99% of emails are legitimate and only 1% are phishing attempts is considered imbalanced. 

Raw data vs. processed data

To better understand the difference, here is a side-by-side comparison of raw and processed data.

Creating a custom dataset takes time and resources. DepositPhotos offers both ready-made and customized AI datasets enriched with metadata and annotations, reducing the workload associated with data preparation. 

Preparing data for AI training

Data preparation is the process of transforming raw data into a training-ready format. It involves collecting, cleaning, organizing, labeling, and formatting data before AI training begins.

Cleaning and removing noise

Data cleaning removes everything that shouldn’t be in the dataset—errors, duplicates, missing values, irrelevant information, and sensitive or harmful content. While much of this process can be automated, human review is still needed to catch subtle errors and resolve ambiguous cases. Common cleaning methods include:

Structuring data for AI models

At first glance, structuring data may seem like a simple task. You standardize file formats, unify naming rules, and organize everything into the right structure. In practice, it involves much more than that.

Creating input-output training examples

For LLM training specifically, structuring data also requires creating input-output pairs. These pairs show the model what a correct response looks like for a given prompt.

For example:

• Input: “Summarize the following article in three bullet points.”
• Output: “Revenue increased by 15% year over year. Customer retention improved to 92%. The company plans to expand into new markets.”

Input-output pairs teach the model not only what to say, but also how to respond, including tone, phrasing, formatting, and structure.

Formatting data for instructions or preferences

For more advanced LLM training, data often needs to be formatted for specific training methods such as supervised fine-tuning (SFT) and preference-based learning. 

Modern LLM pipelines typically start with instruction tuning, a form of SFT that teaches models to follow prompts. They then use preference-based methods to improve response quality.

Instruction tuning teaches models to follow specific commands rather than just predicting the next word.

For example:

• Input: “Translate this idiom to German: kill two birds with one stone.”
• Output: “Zwei Fliegen mit einer Klappe schlagen.”

Preference-based learning trains models using human feedback. Multiple responses are generated for the same prompt and ranked by quality. The model then learns to prefer better responses over weaker ones. This is done using methods such as reinforcement learning from human feedback (RLHF) and direct preference optimization (DPO).

For example:

• Input: “Explain machine learning to a beginner.”
• Output A: “Machine learning is a branch of AI.”
• Output B: “Machine learning is a branch of AI that enables systems to learn from data and improve their performance without explicit programming.”

In this case, output B would be preferred because it is more informative and complete.

Together, these approaches help the model learn not just how to respond, but how to respond well.

Ensuring the quality of the dataset

When creating custom AI training datasets, quality standards should be defined upfront. This includes setting acceptable file formats, labeling guidelines, annotation rules, and coverage requirements.

DepositPhotos’ multimodal datasets are a strong example of this approach. With over 330 million files, each asset is carefully curated and enriched with accurate metadata, including descriptions, keywords, image types, and more. It provides a great alternative to creating a dataset from scratch.

Annotation and labeling consistency

Consistent annotations are critical for building reliable AI models. When the same type of data is labeled differently across the dataset, the model receives conflicting signals. As a result, it is harder for the model to learn correct patterns and produce stable outputs.

Maintaining this consistency is made harder by the fact that annotation requirements differ by modality. Text is usually labeled for sentiment, intent, or categories, images for objects or regions, audio for speech or sounds, and video for actions or sequences.

Multimodal data additionally requires connecting information across different types so the model understands how they relate. For example:

• Image + description of what it shows
• Audio + transcript matching spoken words
• Video + caption describing what is happening

Validation and quality checks

Even with clear quality standards in place, ongoing validation is essential. This includes reviewing samples, checking for labeling errors, and ensuring consistency across the dataset. Regular quality checks help identify issues early and maintain data reliability.

Improving dataset coverage

Beyond consistent annotations, high-quality AI and LLM datasets should also provide strong coverage. Dataset coverage measures how well the data reflects real-world conditions. This helps reduce blind spots in the model’s understanding and decision-making.

Adding edge cases and missing scenarios

Even a well-structured dataset can have gaps. One way to address these gaps is to add scenarios that don’t fit standard patterns. These can be rare or unusual inputs that fall outside of the typical range of training data. Including them improves the model’s ability to deal with situations it hasn’t seen before.

When training LLMs, classic edge cases include:

• Rare and low-resource languages (languages that lack large digitized text datasets)
• Long-tail requests (niche or highly specific user queries)
• Informal or slang-heavy text
• Domain-specific prompts (medical, legal, technical)
• Noisy or unstructured inputs (typos, broken grammar) 
• Culturally diverse content (different norms, shared cultural knowledge)

Quick answer: What is an edge case?

In AI training, an edge case is an uncommon situation that tests how well a model responds to unexpected inputs. 

Finalizing the dataset for training

Once the data has been properly prepared, the dataset is almost ready for training. The final step is splitting it into training, validation, and test sets, which is essential for evaluating model performance.

Training set vs. validation set vs. test set

A typical dataset split looks like this:

• Training set: The largest portion of the data, usually 70–80%, used to train the model
• Validation set: A smaller portion of the data, typically 10–15%, used during training to fine-tune the model
• Test set: Another 10–15% of the data, used after training to evaluate the model’s performance on unseen data

It is important to ensure that there is no overlap between these sets, especially between the training and test sets. If the model has already seen the test data during training, its performance will appear better than it actually is—a problem known as benchmark contamination.

Bottom line

Training data defines not only what a model learns but how it performs. Every decision made during the dataset creation process shapes the model’s behavior, accuracy, and real-world reliability. 

Rigorous sourcing, cleaning, structuring, and labeling are direct investments in model quality. Licensed sources ensure secure development. Diverse data improves generalization. Multimodal datasets expand use cases. Clean annotations reduce training time. Balanced distribution minimizes bias.

Whether you build a dataset from scratch or leverage ready-made solutions like DepositPhotos’ AI training datasets, the core principle remains unchanged: the better the data, the better the model. The best time to get it right is before training begins.

FAQ

What is the minimum amount of data needed to train an LLM model?

There is no universal minimum amount of data required, as it depends on the model type and complexity. In traditional machine learning, a common guideline is to have at least 10 times more training examples than features, though deep learning models typically require much larger datasets. 

What is the cost of training LLM models?

The cost of training LLM models depends on many factors, including model size, dataset volume, compute infrastructure, and training duration. Small to mid-size LLMs can cost $10,000–$500,000, while large-scale models like GPT-3 are estimated to cost around $2–$4 million in compute alone.

What are the top LLM training companies?

Several companies are leading the development of large language models today, including OpenAI (GPT), Google DeepMind (Gemini), Anthropic (Claude), Meta AI (LLaMA), Microsoft (Copilot), Amazon Web Services (Titan), Mistral AI, and Cohere.