# Before You Share an AI Product Launch: What to Verify > MKNIRALO — AI & Tools — EDITORIAL TOOLKIT - Published: 2026-10-30 - Updated: 2026-10-31 - Estimated reading time: 7 minutes - Byline: Deo — Technology Enthusiast - Byline disclosure: Deo is a house editorial byline maintained by MKNIRALO, not a claim of an independently verified individual. It does not imply personal product testing or professional credentials. - Author profile: https://mkniralo.com/authors/deo/ - Tags: AI, AI Tools, Product Launch, Technology, Fact Checking - Cover image: https://mkniralo.com/images/ai-product-launch-checklist.svg Check release status, account and regional access, pricing, product limits, privacy and evidence before repeating an AI launch claim. An AI product announcement can describe a working feature, a limited experiment, a future plan or a marketing promise. Those are not the same thing. Before sharing a launch story, establish what people can actually use, who can access it and what evidence supports the claims. ## Separate the announcement from the release Start with the dated primary announcement, product documentation, changelog or developer release notes. Record the exact product name, feature name, model or version, announcement date and any stated rollout timeline. Treat release language as a status label that needs context. “Announced” may mean only that a company has described a plan. A waitlist means access has not yet been granted. An alpha or beta may be limited to selected testers. A preview may be restricted by region, plan or account eligibility. “Generally available” is stronger, but you should still check the actual eligibility and supported locations. These labels are not standardized across every vendor. Quote the provider’s wording, explain what it means in practice and avoid upgrading a limited rollout into “available to everyone.” ## Build an access and availability matrix Check availability separately for each access path: web app, desktop app, mobile app, API, developer platform, enterprise workspace and integrations. A feature may exist in one surface but not another. Record the supported countries or regions, eligible account types, subscription tiers, age or organization requirements, language support and any waitlist or approval process. If the provider says rollout is gradual, do not infer that every eligible user has received access. A useful sentence is precise: “The company says the feature is rolling out to eligible paid accounts in selected regions.” That is more informative than “The feature is now available” when access remains limited. ## Verify pricing beyond the headline Look for the actual pricing page and plan documentation. Check whether the feature is included, offered as a trial, charged by seat, billed by usage, limited by credits or tokens, or reserved for enterprise customers. Record quotas, rate limits, overage charges, API pricing, premium-model surcharges and any separate fees for connected services. Distinguish “free to try” from “free to use without limits.” If a price is not published, say that it was not disclosed rather than estimating it. Use the date and currency shown by the provider. Do not assume a price in one country, plan or billing cycle applies globally. ## Test what the feature actually does Translate broad claims into observable tasks. If a product says it can analyze documents, check the supported file types, file-size limits, page limits, language support and whether it can cite the source passages. If it claims to automate a workflow, identify which steps it can perform and which still require a person. Where hands-on access is available, test a small set of representative tasks and record the product version, account tier, date, inputs and evaluation method. Include a normal case and an edge case. Keep screenshots or notes only when you have permission to retain and publish them. If you cannot access the feature, do not imply that you tested it. State that the description is based on company documentation and separate those claims from independent evaluation. A polished demo is evidence that a demo was shown, not proof that every customer can reproduce the result. Sources: - [Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile](https://www.nist.gov/publications/artificial-intelligence-risk-management-framework-generative-artificial-intelligence) — National Institute of Standards and Technology (NIST) - [OWASP Top 10 for LLM Applications 2025](https://genai.owasp.org/resource/owasp-top-10-for-llm-applications-2025/) — OWASP Gen AI Security Project ## Inspect limitations, privacy and safety information Read the product documentation for supported and unsupported use cases, known limitations, regional restrictions, data retention, training or model-improvement settings, human review, connected-service permissions and deletion controls. For business use, check whether the terms differ by plan and whether an administrator can control access. For generative AI products, NIST’s Generative AI Profile provides a voluntary risk-management lens, while OWASP’s LLM Top 10 describes application risks such as prompt injection, sensitive-information disclosure, misinformation and excessive agency. These are frameworks for asking better questions; neither is a certification that a particular product is safe. Sources: - [Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile](https://www.nist.gov/publications/artificial-intelligence-risk-management-framework-generative-artificial-intelligence) — National Institute of Standards and Technology (NIST) - [OWASP Top 10 for LLM Applications 2025](https://genai.owasp.org/resource/owasp-top-10-for-llm-applications-2025/) — OWASP Gen AI Security Project ## Treat performance and benchmark claims as claims When a company says a model is “the most accurate,” “human-level” or “twice as fast,” look for the benchmark, comparison baseline, model version, test set, evaluation method, sample size and date. Ask whether the test measures the task your readers care about and whether the result has been independently reproduced. The U.S. Federal Trade Commission says advertising claims must be truthful, non-deceptive and evidence-based. Its AI-related enforcement actions show that marketing a service as AI-powered does not remove the need to substantiate what the product can do. Do not turn a company’s superlative into an editorial fact. Sources: - [Advertising and Marketing Basics](https://www.ftc.gov/business-guidance/advertising-marketing) — U.S. Federal Trade Commission - [FTC Announces Crackdown on Deceptive AI Claims and Schemes](https://www.ftc.gov/news-events/news/press-releases/2024/09/ftc-announces-crackdown-deceptive-ai-claims-schemes) — U.S. Federal Trade Commission ## Choose sources in the right order Use primary sources for release details: official announcements, documentation, changelogs, pricing pages and support articles. Use independent reporting and testing to assess significance, limitations and real-world performance. User posts can reveal rollout problems or user experiences, but they are leads to verify, not a substitute for release documentation. When sources conflict, show the discrepancy and the date of each source. A help page may lag behind a product announcement; a social post may describe a limited test rather than general access. Do not silently choose whichever version makes the launch sound bigger. Sources: - [Advertising and Marketing Basics](https://www.ftc.gov/business-guidance/advertising-marketing) — U.S. Federal Trade Commission - [FTC Announces Crackdown on Deceptive AI Claims and Schemes](https://www.ftc.gov/news-events/news/press-releases/2024/09/ftc-announces-crackdown-deceptive-ai-claims-schemes) — U.S. Federal Trade Commission - [Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile](https://www.nist.gov/publications/artificial-intelligence-risk-management-framework-generative-artificial-intelligence) — National Institute of Standards and Technology (NIST) ## A publication checklist 1. What exactly was announced, and on what date? 2. Is the feature announced, waitlisted, alpha, beta, preview or generally available? 3. Which countries, account tiers, platforms and users can access it today? 4. What does it cost, and what usage limits or extra charges apply? 5. Which model or product version is involved? 6. What can the feature do, and what are its documented limits? 7. Have data use, retention, permissions and safety controls been checked? 8. Are performance claims supported by a clear method and evidence? 9. Was the product tested directly? If not, is that limitation stated? 10. Are primary sources linked close to the claims they support? Before publication, keep a dated note of what was confirmed and what remains unknown. Recheck access, pricing and rollout details immediately before the article goes live; launch information can change between the announcement and publication. Sources: - [Advertising and Marketing Basics](https://www.ftc.gov/business-guidance/advertising-marketing) — U.S. Federal Trade Commission - [FTC Announces Crackdown on Deceptive AI Claims and Schemes](https://www.ftc.gov/news-events/news/press-releases/2024/09/ftc-announces-crackdown-deceptive-ai-claims-schemes) — U.S. Federal Trade Commission - [Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile](https://www.nist.gov/publications/artificial-intelligence-risk-management-framework-generative-artificial-intelligence) — National Institute of Standards and Technology (NIST) - [OWASP Top 10 for LLM Applications 2025](https://genai.owasp.org/resource/owasp-top-10-for-llm-applications-2025/) — OWASP Gen AI Security Project ## About this article This article reflects the public sources available on the publication and update dates shown above. For developing stories, follow the linked source pages for subsequent statements or decisions. ## Sources and further reading - [Advertising and Marketing Basics](https://www.ftc.gov/business-guidance/advertising-marketing) — U.S. Federal Trade Commission (accessed 2026-10-31) - [FTC Announces Crackdown on Deceptive AI Claims and Schemes](https://www.ftc.gov/news-events/news/press-releases/2024/09/ftc-announces-crackdown-deceptive-ai-claims-schemes) — U.S. Federal Trade Commission (accessed 2026-10-31) - [Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile](https://www.nist.gov/publications/artificial-intelligence-risk-management-framework-generative-artificial-intelligence) — National Institute of Standards and Technology (NIST) (accessed 2026-10-31) - [OWASP Top 10 for LLM Applications 2025](https://genai.owasp.org/resource/owasp-top-10-for-llm-applications-2025/) — OWASP Gen AI Security Project (accessed 2026-10-31)