How to collect training data for AI models

What is AI model training data?

AI model training data is a set of examples that's used to shape what a model learns to do. That data might include text, code, images, audio, video, or structured records. It can also include class labels, expert demonstrations, or judgments comparing possible answers.

Not every training method requires human labels, and data retrieved at query time is different from data used to change a model’s parameters. A held-out dataset used to measure performance serves a different purpose, too. Understanding what AI training data is can help you clarify which examples represent the work your model must perform.

What makes AI training data high quality, and why is it important?

High-quality training data is fit for the task the model needs to perform. For instance, a dataset for reviewing contract clauses should cover the contract types, jurisdictions, document formats, and judgments relevant to that use case. Its inputs and any target answers also need to be accurate enough to support the intended behavior. Google’s data-characteristics guidance emphasizes relevant filtering, reliable labeling, and proper handling of missing or duplicate examples as important parts of building a high-quality dataset.

Why training data quality matters becomes clearer when you consider a hypothetical medical dataset containing many records but almost none from a patient group the product must serve. Despite its size, the dataset wouldn't be suitable. Teams should also check for consistency, completeness, recency, and traceable origins.

Teams should assess factors such as:

• Relevance to the intended task
• Accuracy of inputs and target outputs
• Coverage of important scenarios
• Consistency and completeness
• Recency where the task requires current information
• Traceable provenance
• Appropriate permissions for the intended use

Datasheets for Datasets recommends documenting a dataset's composition, collection, intended uses, and limitations.

Quality and permissions should be evaluated separately. A technically strong dataset may still be unsuitable if the intended use is not permitted.

How does the AI training data collection process work in 6 steps?

Learning how to collect training data for AI starts with having a clearly defined task and a process for reviewing the data before scaling. The following 6 steps provide a practical framework for collecting and preparing AI training data. Each step should produce an output your team can review before they move to the next stage or scale the process.

Step 1: Define model objectives and plan

Start by specifying:

• The task the model needs to perform
• The target domain
• Intended users
• Input and output formats
• Acceptance criteria
• Budget and deadline
• Dataset owner
• Whether the work involves adapting an existing model or broader training

The output should be a collection specification that includes examples of acceptable and unacceptable results. This gives the team a standard for judging every potential source later in the process.

Step 2: Identify and source raw inputs

Identify approved internal records, suitable open resources, and vendor samples. If there are gaps, you may need to build custom training datasets from commissioned expert examples or synthetic candidates. Record where each source came from and check its permitted uses before collection. The result should be a shortlist of potential sources for later comparison.

Step 3: Clean and standardize data

Remove irrelevant, corrupted, or duplicate material. Address missing values appropriately, and standardize schemas and units without removing meaningful variation. Maintain a repeatable preparation process and a verified sample. Teams should also define the training, validation, and test splits early to prevent related records from being included in held-out evaluation data. Google provides additional guidance on how training, validation, and test data serve different purposes.

Step 4: Label or annotate data

AI data annotation can include class labels, expert demonstrations, preference comparisons, or task-specific rubrics. Provide clear instructions and example decisions, then define how reviewers should resolve disagreements. It's important to use qualified experts for domain-specific judgments, but not every training method will require manual annotation. Mercor’s explanation of the data labeling process provides more detail on these techniques.

Step 5: Validate and review the dataset

Review a representative sample for correctness, domain coverage, provenance, sensitive content, and duplication across splits, and have domain experts resolve any uncertain cases. Reviewer agreement can indicate whether instructions are clear, but it doesn't necessarily prove that the response is correct. Record whether the examples should be accepted, revised, or rejected, then test a small pilot on held-out data.

Step 6: Document and monitor

Record the dataset's sources, permissions, collection dates, transformations, label rules, versions, intended uses, and known gaps. Assign an owner responsible for corrections and updates, and maintain a change log. A dataset card can help organize this information; Hugging Face’s dataset-card documentation suggests including fields such as purpose, composition, and license metadata.

Reassess the dataset whenever the task or underlying data changes. Once teams know how to collect and prepare training data, they can compare the different sources and procurement options available.

What are the main sources of AI training data?

A data source describes where training examples come from, while a procurement route describes how a team obtains them. Teams might build a dataset from internal records, use a public dataset, license a prebuilt package, commission new examples, or combine several approaches when one source leaves a coverage gap. Buying data often involves buying the licensing rights to use it rather than transferring actual data ownership.

The table below compares the main procurement routes:

Documenting the origins and intended uses of datasets is essential for sound dataset selection. Mercor’s commercial catalog illustrates prebuilt packages and custom extensions, but it's important to review each package's samples and terms before choosing a source.

Public and open datasets

Repositories such as Hugging Face can help teams discover candidates for open-source AI training data. Make sure to review each dataset’s card, license metadata, sampling method, and domain coverage before use. A dataset being publicly available doesn’t automatically mean it can be used for unrestricted commercial training or guarantee thorough documentation.

Public web data

Accessible web material can often provide task-specific examples. However, accessibility doesn't equate to permission. Common Crawl’s terms address third-party claims involving crawled content and AI use. Teams should review the source, applicable terms, and proposed use rather than assuming all web data is freely usable or prohibited.

Enterprise and commercial data providers

Various companies offer AI training data. Mercor, for example, advertises prebuilt expert data and custom projects, You may also find options through AI training data marketplaces. Ask providers for representative samples, source records, reviewer qualifications, quality evidence, package-specific rights, and total delivered cost. Make sure to distinguish between existing licensed packages and examples created to your specifications.

Enterprise internal and proprietary data

An organization’s own approved records, work products, and expert demonstrations may reflect its workflows better than generic data. Before repurposing this data, check confidentiality, reuse permissions, access controls, and sensitive-data handling requirements. Owning the system that stores a document doesn't automatically grant unrestricted rights to every record or third-party contribution it contains.

Synthetic data

Model- or simulation-generated examples can help fill known gaps when real examples are scarce. Track how the data was created, filter out errors and duplicates, and validate the results against the real task. Synthetic examples should supplement representative data rather than be assumed to reflect every domain or use case accurately.

Understanding the available options is only one part of the sourcing decision. Teams still need to test whether a source meets quality, cost, and rights requirements before scaling.

Best practices for high-quality AI data collection

A small sourcing pilot can reveal potential quality, cost, or rights problems before they become expensive to correct. The following practices can help teams catch these problems early and make better sourcing decisions:

• Test representative samples first: Compare samples from shortlisted sources against the same acceptance criteria. Protect a held-out evaluation set from training and repeated tuning so the team has an independent way to assess performance.
• Measure the full cost of usable data: Compare the cost of producing each accepted usable example, including the cost of expert review, rework, preparation, and integration. Considering the upfront price alone can mask the cost of fixing weak or unsuitable data.
• Set clear release gates: Require task-quality approval, appropriate data rights, and a named maintenance owner before scaling. Record the dataset's limitations and permitted uses in a datasheet, and adjust the source mix or collection instructions if the pilot fails to meet expectations.

These checks can prevent teams from committing more time and budget to weak sourcing approaches.

Common challenges in AI data collection

Even a well-planned data collection process can break down when the available data is incomplete, difficult to review, or has unclear reuse rights. Teams may need to adjust their sourcing strategy as these problems surface. Most issues tend to fall into 3 areas:

• Scarce examples and conflicting expert judgments: Important scenarios may be underrepresented, while qualified reviewers may disagree about the correct label or outcome. Target missing cases, clarify the labeling rubric, and resolve substantive disagreements rather than masking them with average scores.
• Unclear permissions or confidential material: Quarantine data when reuse rights or confidentiality requirements are unresolved, and route the decision to the appropriate data or legal owner. Datasheets for Datasets highlights questions about collection, sensitive information, intended use, and maintenance that can help with these issues.
• Slow review, rework, or stale data: An inexpensive source can become costly when examples require repeated correction or no longer reflect the task. Collect data in stages, inspect each batch, and adjust the mix of internal, licensed, and commissioned examples as needed.

More data will not solve a sourcing problem if the underlying examples don’t fit the task.

Mercor’s off-the-shelf commercial AI datasets for training, post-training, and evaluation

Mercor’s commercial dataset catalog includes ready-to-license datasets for model training, post-training, or further adaptation of an already pretrained model. Some offerings may share names with public evaluation assets, so teams should confirm the selected package’s contents and permitted uses and ensure that any benchmark test data remains separate.

APEX Agents

Mercor offers commercial APEX-Agents data for long-horizon professional work in realistic enterprise-style environments. Teams can review samples to see whether the files, applications, and outputs match the workflows they want a model to learn.

The public APEX-Agents evaluation methodology covers investment banking, consulting, and corporate-law workflows assessed with rubric-based grading. Its public dataset card limits its use to evaluation and prohibits training or fine-tuning. APEX-Agents public benchmark data isn't part of the commercial training package.

SWEBench Ext.

Mercor describes SWEBench Ext. as commercial data for repository-scale software engineering tasks, including resolving GitHub issues in containerized environments with verified tests and multifile patches. This dataset may fit teams training coding agents to work across real repositories.

The public SWE-bench serves a different purpose. Its evaluation guide explains how generated patches are applied and tested against repositories, while the original dataset card includes train, development, and test splits. Teams should confirm the commercial package’s rights and check for overlap with any evaluation data they plan to use.

TerminalBench

Mercor’s catalog explains that TerminalBench features commercial data for multistage terminal and repository workflows, including checks for partial or destructive outcomes. It may suit teams training agents to complete tool-driven tasks in technical environments.

The public Terminal-Bench project is an evaluation benchmark for agent work in terminal environments and explicitly warns against including benchmark data in training datasets. Teams should treat the public benchmark and Mercor’s commercial offering as separate assets with separate permissions.

BrowseComp

Mercor lists commercial BrowseComp data for browsing and search tasks that require agents to plan searches, navigate sources, and synthesize grounded answers. Teams should request examples that match their intended research workflow and confirm exactly what the licensed package contains.

OpenAI’s public BrowseComp benchmark evaluates whether agents can find difficult-to-locate information and verify answers against references. Its creators also use measures intended to reduce benchmark leakage into training datasets. Those public evaluation materials should remain distinct from Mercor’s commercial dataset and its licensing terms.