AI training data claims: what should buyers ask?
Last reviewed June 2, 2026
Training data claims often sit behind short phrases like trained on proprietary data, learns from your knowledge base, or does not train on customer data. This page maps those phrases to the provenance, cutoff, retrieval, fine-tuning, feedback, customer-data-use, and model-update evidence a buyer should request.
Evidence buyers verify
- A training-data source inventory tied to the exact model or product version named in the claim.
- A training cutoff, update cadence, or retrieval-source freshness rule for time-sensitive outputs.
- A clear customer-data boundary: prompts, files, outputs, logs, feedback, fine-tuning files, and connected-app data.
Opens the checker for this claim type. Paste your vendor's exact wording there. Evidence questions only — not a blacklist or fraud detector. Not sure what a result looks like? See a sample receipt.
Sources this guide draws from
- NIST AI Risk Management Framework 1.0 NIST standard· January 26, 2023
Official framework source for mapping AI capability claims to documented context, measurement, limitations, and risk-management evidence.
- EU AI Act Article 10: Data and data governance European Commission AI Office standard· Official version June 13, 2024; accessed June 1, 2026
Official EU AI Office service-desk text for training, validation, and testing data governance requirements for high-risk AI systems.
- OpenAI business data page OpenAI company-page· Accessed June 1, 2026
Public company source for no-training-by-default, model training sources, and business-data boundary wording.
- Intercom Fin AI Agent explained Intercom company-page· Accessed June 1, 2026
Public company source for Fin learning from public and private knowledge sources, content libraries, and data connectors.
Public claims with documented evidence gaps
"We don't train our models on your organization's data by default"
Compliance / Safety- Source and date
- OpenAI business data page · Accessed June 1, 2026
- Evidence signal
- No-training wording qualified by by default, which means product, plan, opt-in, API, and fine-tuning boundaries matter.
- Evidence gap
- A buyer needs covered products, inputs and outputs included, opt-in settings, fine-tuning terms, subprocessor access, and DPA language for the specific deployment.
- Buyer question
- For the no-training-by-default claim, which products, API configurations, fine-tuning workflows, and opt-in settings are inside or outside the boundary?
"publicly available knowledge on the Internet, data provided through third-party partnerships, and information that our researchers provide or generate"
Vague AI-powered- Source and date
- OpenAI business data page · Accessed June 1, 2026
- Evidence signal
- Training-source disclosure that lists broad source categories without showing what applies to a specific model or product claim.
- Evidence gap
- A buyer needs the model version, training cutoff, data category relevance, exclusion process, and whether the vendor product adds product-specific training or retrieval.
- Buyer question
- For this training-source claim, which model version and cutoff date apply to the product we would use, and what product-specific data is added after the base model?
"Fin can learn from a variety of public and private knowledge sources"
Vague AI-powered- Source and date
- Intercom Fin AI Agent explained · Accessed June 1, 2026
- Evidence signal
- Learning-from-sources wording that could mean retrieval, indexing, fine-tuning, or workflow-specific grounding unless the mechanism is defined.
- Evidence gap
- A buyer needs a source inventory, whether data is retrieved or used for model training, sync cadence, access rules, and deletion behavior when a source is removed.
- Buyer question
- For the learns-from-sources claim, does learn mean retrieval from allowed sources, model fine-tuning, or a shared model update, and how is each source governed?
"keeping answers accurate and complete as your business changes and grows"
Accuracy / Performance- Source and date
- Intercom Fin AI Agent explained · Accessed June 1, 2026
- Evidence signal
- Freshness claim tied to changing business content without showing sync timing, stale-source handling, or accuracy measurement.
- Evidence gap
- A buyer needs source sync cadence, content approval workflow, stale-content monitoring, answer accuracy checks, and logs showing which source produced an answer.
- Buyer question
- For the accurate-as-business-changes claim, how quickly do source updates affect answers and what test shows old content is no longer used?
Match each claim pattern to the evidence buyers need
| Claim pattern | Evidence needed | Buyer question |
|---|---|---|
| We do not train on your data | Product and plan scope, opt-in settings, API and fine-tuning terms, input/output coverage, retention terms, and DPA language. | Which product surfaces and configurations are excluded from model training, and what changes if we enable fine-tuning or feedback sharing? |
| No training on customer data, not used to improve models, or no training by default | Covered products, API/app boundary, prompt and output handling, feedback setting, evaluation use, abuse-monitoring review, fine-tuning terms, and opt-in records. | Does no training exclude prompts, outputs, files, feedback, logs, evaluation data, and fine-tuning files for the product we would use? |
| Trained on proprietary, domain, customer, or expert data | Data source inventory, original purpose, licensing or permission basis, collection date, labeling process, representativeness, and gaps. | What data sources were used, when were they collected, and how do they represent the domain we would rely on? |
| Learns from your documents, knowledge base, or private sources | Retrieval versus training mechanism, source access controls, sync cadence, deletion process, and answer-source audit trail. | Does learn mean retrieval at answer time or model training, and what happens when a source is deleted or permission changes? |
| Feedback improves the AI or the AI gets better from user interactions | Feedback collection method, opt-in setting, review process, training or evaluation use, retention period, tenant isolation, and deletion behavior. | Does feedback from our users train a shared model, tune our private configuration, support evaluation, or only improve source retrieval? |
| Always current, real-time, or up to date | Training cutoff, retrieval coverage, update cadence, stale-source detection, and domains where freshness is not supported. | What knowledge boundary or training cutoff applies, and which real-time sources supplement it? |
| High-quality training, validation, and testing data | Data governance process, validation and test set separation, bias and gap assessment, annotation rules, and context-of-use match. | How do the training, validation, and test datasets differ, and which one supports the public claim? |
| Proprietary or domain training data improves AI performance | Data source inventory, relevance to the buyer's domain, benchmark comparison, ablation or baseline result, and limits where the proprietary data does not apply. | What result shows the proprietary data improves the task we care about, compared with a model without that data? |
Evidence to request
- A training-data source inventory tied to the exact model or product version named in the claim.
- A training cutoff, update cadence, or retrieval-source freshness rule for time-sensitive outputs.
- A clear customer-data boundary: prompts, files, outputs, logs, feedback, fine-tuning files, and connected-app data.
- A distinction between retrieval, fine-tuning, evaluation, feedback review, abuse monitoring, and shared model training.
- Data governance evidence for collection origin, preparation, labeling, bias review, gaps, and suitability for the intended purpose.
- A deletion and opt-out process that explains what happens to source data already indexed, retrieved, or used in fine-tuning.
Questions to put in front of the vendor
- For this AI training data claim, which exact data sources trained the model or grounded the product output?
- What is the model version and training cutoff date behind the claim?
- Does the vendor use customer prompts, files, outputs, feedback, or logs for shared model training by default?
- Does feedback, thumbs-up/down data, support review, or evaluation data train a shared model or only affect our private configuration?
- If the product learns from our knowledge base, is that retrieval, fine-tuning, or another update process?
- Which validation and testing datasets support the performance claim, and are they separate from the training data?
- What source deletion, opt-out, and access-control process applies after our data is connected?
Wording boundaries to compare against
- Business inputs and outputs are not used for shared model training by default for named products and configurations.
- The model uses retrieval from allowed customer sources at answer time; sources are not used to train a shared model unless separately enabled.
- Model version [X] has a training cutoff of [date]; current information is retrieved from [named sources] when available.
- Training, validation, and test data are documented for [use case], with known data gaps and limitations stated.
Frequently asked questions
- Does proprietary training data prove better AI performance?
- No. Proprietary or domain data is only useful evidence if the vendor shows what data was used, when it was collected, how it maps to your task, and what benchmark or customer sample improved because of it.
- Should vendors disclose whether customer data trains the model?
- A buyer should ask which products, prompts, files, outputs, logs, feedback, fine-tuning data, and connected sources are used for shared model training, product-specific training, retrieval, evaluation, or support review. The answer should be tied to the product and configuration you would use.
- What is the difference between retrieval and model training?
- Retrieval uses allowed sources at answer time, while model training changes model behavior through training or fine-tuning data. A learns-from-your-data claim should state which mechanism is used, what access controls apply, and what happens when a source is removed.
- What does no training on customer data actually mean?
- Ask which customer data categories are excluded: prompts, files, outputs, feedback, logs, fine-tuning files, connected sources, and evaluation data. The answer should name products, endpoints, opt-in settings, DPA language, and exceptions such as support review or abuse monitoring.
- Does feedback train the model?
- Only if the vendor says how feedback is used. Feedback may support shared model training, tenant-specific configuration, answer-quality evaluation, support review, or source retrieval. A buyer should ask which use applies and whether it can be disabled.
Have your vendor's exact claim wording ready?
Check an AI training data claim How the evidence method works