Data gives generative-AI models the examples from which they learn patterns, and it is used to refine and evaluate them. Large datasets help models develop broad capabilities, but volume alone does not make a model reliable: relevance, quality, diversity, representativeness, provenance, privacy and governance all matter. Algorithms and computing power matter alongside data.
What data does for a generative-AI model
During training, a model processes examples and adjusts its parameters to learn statistical relationships—for example, how words tend to follow one another or how image features relate to text. The result is a model that can generate new outputs from a prompt. It is not simply looking up a matching answer in a conventional database, though a model may reproduce details from training examples.
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Data also helps developers refine model behavior and check how well a system performs. In reinforcement learning from human feedback, for example, people rank or compare candidate responses; those judgments can be used to steer outputs. Separate evaluation data and tests help assess capabilities, accuracy and safety.
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The European Commission’s Joint Research Centre (JRC) described data as “the lifeblood of GenAI,” calling it the primary input for training, refining and validating AI models in its 2025 Generative AI Outlook Report. Data is foundational, but it does not work alone: the U.S. Government Accountability Office (GAO) identifies datasets, advances in deep-learning algorithms and computing capacity as joint enablers of generative AI.
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What kinds of data can be involved
A training pipeline may draw on several sources and formats. The exact mix varies by developer and model, and commercial developers often disclose only high-level information rather than a complete dataset inventory.
- Publicly available internet material: text, images and other content accessible online. Public availability does not by itself establish that material is free of copyright, privacy or other restrictions.
- Licensed or otherwise permissioned collections: material made available under agreements or other defined terms.
- Proprietary records: an organization’s internal documents or other records, where use is authorized and appropriate safeguards are in place.
- Human feedback: ratings, rankings or comparisons that help shape a model’s responses.
- Multimodal examples: combinations of text, images, audio or other formats, depending on the model and task.
Do not assume that a named commercial model used a particular collection unless the developer’s own documentation establishes it. GAO notes that training datasets can range from millions to trillions of data points, while developers may reveal little beyond broad descriptions.
How data moves through the model lifecycle
Collection and sourcing
Developers gather or obtain material suited to a model’s intended capabilities. The source and conditions of use matter: they affect whether the data is relevant, lawfully usable, sufficiently representative and safe to handle. Public scraping can also collect personal or sensitive information, whether or not that was the intended purpose.
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Curation and filtering
Before training, data may be filtered, deduplicated, labeled, organized or removed. Curation can reduce harmful or sensitive content and improve consistency, but it cannot guarantee that a dataset is free of errors, bias or risky material. Privacy evaluations may take place at multiple points in development.
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Training and alignment
During training, the model learns patterns from the selected examples. Later refinement may use human judgments or other methods to encourage more useful outputs. The quality of these inputs and the way they are used influence what the model can do—and where its limitations remain.
Validation and evaluation
Developers can test models using held-out data, benchmarks, multidisciplinary review and red teaming. These methods probe accuracy, context handling, safety and security, but passing tests does not establish that a model will be reliable in every situation. GAO reports that developers acknowledge ongoing reliability limitations.
Deployment and monitoring
Data governance remains important after release. Organizations using or adapting models should track data provenance and versions, restrict access appropriately, manage updates, and monitor for drift, bias, privacy leakage, poisoning and unintended reliance on synthetic data. These are practical safeguards against risks identified by GAO, the JRC and the EU AI Act’s data-governance provisions.
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Why quality matters more than volume alone
A very large dataset can still be a poor fit for a task. If its examples are inaccurate, incomplete, irrelevant or skewed toward particular populations, languages or domains, adding more of the same may preserve or amplify those weaknesses. Data that better represents the intended users and situations can improve usefulness and help reduce bias, though it cannot eliminate every source of error.
EU AI Act Recital 67 says high-quality data plays a vital role in the performance of many AI systems, especially those that use model training. It says training, validation and testing datasets should be relevant and sufficiently representative, and as error-free and complete as possible. The recital also points to data governance that addresses privacy and bias.
When assessing a dataset or data strategy, examine these dimensions together:
- Task fit: Does the material reflect the job the model is expected to perform?
- Diversity and representation: Are relevant people, languages, domains and modalities adequately represented?
- Accuracy and completeness: Are examples correct, consistent and sufficiently complete for their use?
- Provenance and permissions: Can the organization identify where the data came from and the terms under which it may be used?
- Privacy and security: Could data expose personal or sensitive information, or be altered by an attacker?
- Documentation and maintenance: Are versions, transformations and updates recorded so that issues can be traced?
- Evaluation coverage: Do tests cover the people, conditions and edge cases that matter for the intended deployment?
- Operational cost: Can the organization afford the compute, review and ongoing maintenance required?
How much data does generative AI need?
There is no single data-volume threshold that applies to every model. The GAO’s 2024 account describes training datasets spanning millions to trillions of data points. That range illustrates scale, not a universal requirement or a guarantee of quality. The amount needed depends on the task, model design, data quality and the rest of the training setup.
Scale also has a computing cost. GAO reports that training large models can involve tens of thousands of processors running for months, and that training costs for large models can potentially reach hundreds of millions of dollars. These are descriptions of large-model training, not a price or duration that applies to every generative-AI project. Processing and updating models also contribute to energy use and environmental concerns.
For a company, the useful question is therefore not simply “How many records can we collect?” It is whether the available data is fit for the intended task, whether its use is properly governed, and whether the organization can train, evaluate and maintain the system responsibly.
Risks of training on internet-scale data
Copyright and other legal exposure
Public web material can include copyrighted works. Its availability online does not settle whether copying or using it to train a model is permitted. The GAO notes copyright concerns around internet information used for training, while EU reporting also identifies intellectual-property and data-protection issues. The applicable rules and circumstances matter; a general description of a dataset is not a legal determination.
Privacy and sensitive information
Scraped material may contain personal information, including details people did not expect to be used in model development. Filtering and privacy evaluations can reduce risk, but organizations should consider what was collected, how it is handled and whether safeguards are appropriate throughout development.
Bias and gaps in representation
Size does not ensure that a dataset represents all relevant groups or situations. Underrepresented communities, languages or less common cases may be poorly served. The JRC also connects distribution shifts and synthetic-data training with weaker prediction of low-probability events—precisely the kinds of cases that may be rare in ordinary data but important in practice.
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Poisoning and manipulation
Attackers may manipulate training data or processes to alter a model’s behavior. Risks do not end at training: GAO also describes prompt injection and jailbreaks as security concerns for deployed generative-AI systems. Data controls and model testing need to reflect the relevant threat, rather than treating all unexpected outputs as a data-quality problem.
Synthetic-data feedback and model collapse
Synthetic data—content generated by AI—can be useful in some workflows, but repeated training on AI-generated material can degrade a model. The JRC reports that this feedback can cause model collapse or rapid performance deterioration through distribution shift. It is therefore important to track whether datasets contain synthetic material and to evaluate how its inclusion affects performance, especially on rare cases.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How companies can govern data for generative AI
A practical governance program turns broad principles into decisions that can be checked and revisited. The following steps are operational recommendations based on the documented risks and data-quality expectations; they are not a claim that one process fits every organization.
- Define the use before acquiring data. Specify the model’s task, intended users, affected groups and deployment context. Use that scope to decide what data is relevant and what risks require evaluation.
- Record provenance and terms of use. Maintain an inventory showing where each dataset came from, what permissions or restrictions apply, and how it was transformed. Escalate unclear rights or sensitive-data questions rather than treating public availability as consent.
- Assess representativeness and quality. Check for missing populations, languages, domains and modalities, as well as errors, gaps and duplication. Document known limitations and decide whether the data is adequate for the use case.
- Apply privacy and security controls. Limit access, handle sensitive information deliberately, and evaluate privacy risks at appropriate development stages. Include defenses against unauthorized changes to data or training processes.
- Keep dataset versions and changes traceable. Record what was added, removed or altered and when. Versioning helps teams investigate unexpected behavior and understand whether an update changed model performance.
- Evaluate beyond average performance. Use held-out tests, benchmarks and red teaming to examine accuracy, safety, context and security. Include relevant edge cases and underrepresented scenarios, and involve the disciplines needed to assess them.
- Monitor the deployed system. Track changes in inputs and outputs, investigate drift and emerging failures, and review whether updates remain fit for purpose. Watch for privacy leakage, bias, poisoning and contamination from synthetic data.
- Account for compute and maintenance. Include the resources needed to process data, evaluate updates and sustain monitoring. A dataset strategy that cannot be maintained may undermine the controls it was designed to provide.
Training data is not the same as data supplied at use time
Training data is used to build or refine model parameters. Data supplied during inference—the point when a trained model responds to a prompt—is a separate category. A system may also retrieve documents at answer time rather than incorporating those documents into the model’s training. These distinctions matter when assessing permissions, privacy, freshness and the likely source of a response.
For example, an organization connecting a model to internal files should govern access to those files and the retrieval system even if the files were not used to train the model. Conversely, a model’s training-data governance does not by itself explain what information a user prompt or connected service exposes during use.
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