Last updated: October 10, 2026
This Editorial Policy explains how the Deep-AI.chat Editorial Team selects, researches, writes, reviews, updates, and corrects content published on deep-ai.chat (the “Website”). It also explains how we use sources, artificial intelligence tools, advertising, and reader feedback.
Our goal is to publish clear, useful, and carefully reviewed English-language information about DeepSeek. This policy applies to our articles, guides, explanations, comparisons, tutorials, code examples, prompts, screenshots, and other editorial materials.
This page should be read together with our Disclaimer, Terms of Service, Privacy Policy, and Cookie Policy.
Our editorial mission
Deep-AI.chat exists to help readers understand and use DeepSeek more confidently. We aim to answer practical questions, explain technical concepts in accessible language, clarify product differences, and point readers to the most relevant official resources.
We create content primarily for readers, not merely to attract search traffic. Topic selection may consider reader questions and search demand, but a page must have a clear informational purpose and provide meaningful value beyond repeating information already available elsewhere.
We do not guarantee that every article will be exhaustive or suitable for every reader. However, we aim for each page to satisfy the purpose stated by its title, distinguish confirmed facts from interpretation, and make important limitations easy to understand.
Editorial scope and independence
Deep-AI.chat is an independent editorial website. It is not owned, operated, authorized, sponsored, endorsed, or controlled by DeepSeek or Hangzhou DeepSeek Artificial Intelligence Co., Ltd.
We cover DeepSeek models, applications, APIs, developer tools, integrations, features, pricing, privacy, security, comparisons, and related artificial intelligence topics when they are relevant to DeepSeek users. We may discuss third-party products when necessary to explain an integration, alternative, comparison, or broader context.
Editorial conclusions are made by our editorial team. Advertisers, technology vendors, product providers, and other third parties do not receive the right to approve, suppress, or rewrite our editorial conclusions.
Who creates our content
Articles are published under the collective byline Deep-AI.chat Editorial Team. This byline represents collaborative work and editorial accountability; it is not an invented individual identity or a claim that a single named person completed every stage of the article.
We do not publish fabricated author names, artificial biographies, fake credentials, or AI-generated profile identities. Individual team members may remain private, but the editorial team remains responsible for the material published under the collective byline.
Every article is reviewed before publication by a second editor who did not prepare the initial draft. The second editor checks the article’s accuracy, sourcing, clarity, scope, disclosures, and consistency with this policy. Publication approval remains a human editorial decision.
Our editorial workflow
Our usual workflow includes the following stages. The amount of research and review may vary according to the complexity and potential impact of the subject.
1. Topic selection
We select topics based on reader needs, common problems, important product changes, gaps in existing coverage, and the usefulness of the subject to people interested in DeepSeek. We avoid creating pages solely to target minor keyword variations when they do not justify a distinct and useful resource.
2. Source collection
Before drafting, we identify the primary questions the page must answer and collect the most authoritative available sources. Time-sensitive facts such as model availability, pricing, limits, feature support, policy terms, and software behavior are checked against current official material whenever possible.
3. Drafting and structure
The initial draft is organized around the reader’s task. We aim to use descriptive headings, direct explanations, clearly labeled steps, and appropriate warnings. We avoid unnecessary length, vague claims, unsupported superlatives, and wording that implies certainty where the evidence is limited.
4. Fact-checking and source verification
Material factual claims are checked against the sources used for the article. Names, dates, versions, prices, limits, feature availability, code parameters, policy statements, and links receive particular attention because they can change or cause practical problems when incorrect.
5. Second-editor review
A second editor reviews the draft before publication. This review may lead to corrections, restructuring, additional sourcing, narrower claims, clearer disclosures, removal of unsupported material, or a decision not to publish.
6. Publication checks
Before publication, we check the title, headings, links, formatting, metadata, image text, and page-level disclosures. We also check that the content does not claim first-hand experience, testing, affiliation, expertise, or certainty that the evidence does not support.
Source standards
We prefer primary sources because they are generally the most direct evidence for product facts, technical behavior, policies, and release information. Our preferred source hierarchy is:
- Official DeepSeek sources: the official DeepSeek website, API documentation, official change logs, pricing pages, status pages, product notices, terms, privacy notices, and support material;
- Official technical and research sources: verified DeepSeek GitHub repositories, verified DeepSeek model cards, licenses, technical reports, and research papers;
- Official platform sources: documentation and listings from the operator of an app store, operating system, cloud platform, software library, repository, or integration discussed in the article;
- Other primary evidence: standards, regulatory material, original datasets, company documentation, direct statements, and source code relevant to the claim;
- Reliable secondary sources: reputable reporting, analysis, or expert material used for context, independent interpretation, or facts that cannot reasonably be established from a primary source alone.
We do not treat search-result snippets, unattributed social posts, copied articles, AI-generated summaries, or websites that do not identify their evidence as sufficient support for an important claim.
When sources conflict, we examine their publication dates, scope, definitions, product versions, and authority. We may present the disagreement, narrow the claim, identify the uncertainty, or omit the claim until stronger evidence is available.
Citations, links, quotations, and attribution
We use contextual links to help readers verify important facts and reach the underlying source. A link should support the statement near it rather than merely lead to a general homepage that does not confirm the claim.
Not every sentence requires a citation. Common explanatory material, editorial transitions, and our own analysis may not have individual links. However, specific technical claims, prices, release information, policy requirements, statistics, quotations, and other verifiable details should be supported where a useful source is available.
We aim to quote only the amount necessary for explanation or criticism, identify the source, and avoid reproducing substantial portions of third-party material. When we translate or summarize a source, we aim to preserve its meaning and link to the original material where possible.
Documentation-based content and hands-on testing
Most Deep-AI.chat content is based primarily on official documentation and other verifiable sources. We do not imply that we personally tested a feature, model, application, API, plan, device, or workflow when we did not perform that test.
Unless an article explicitly describes a hands-on test and its method, readers should understand the page as documentation-based editorial guidance rather than an independent laboratory or product test.
If we publish hands-on observations, we aim to state what was tested, the relevant date, product or model version, interface, account or plan conditions, and important limitations. A single test result is not presented as proof that every user will receive the same result.
We distinguish among:
- Confirmed documentation: information supported by an official or authoritative source;
- Observed behavior: behavior directly seen in a stated test or interface;
- Editorial analysis: our interpretation of the available evidence; and
- Unconfirmed information: claims that remain uncertain and are labeled accordingly or excluded.
Use of artificial intelligence in our editorial process
We may use artificial intelligence tools to assist with research organization, topic exploration, outlining, draft preparation, language refinement, summarization, or editorial checklists. AI assistance does not replace human editorial responsibility.
AI-generated output is not treated as an authoritative source. Before publication, human editors must check material claims against appropriate sources, review the wording, assess whether the article answers the reader’s question, and remove fabricated, unsupported, misleading, or irrelevant material.
We do not intentionally publish automatically generated articles without human review. We do not permit AI tools to make the final decision about an article’s accuracy, recommendation, conclusion, correction, or publication.
Our editorial team remains accountable for the final published version, including its title, body text, links, tables, code, metadata, image alternative text, and disclosures.
Accuracy and fact-checking standards
We aim to use precise language and evidence proportionate to the claim. Editors are expected to distinguish among facts, estimates, opinions, provider claims, reported results, and predictions.
We avoid describing a product as the “best,” “safest,” “fastest,” “cheapest,” or “most accurate” unless the comparison has a defined scope, current evidence, and a fair methodology. When a claim applies only to a particular model, plan, date, region, workload, or pricing condition, we aim to state that limitation.
For subjects involving privacy, security, legal terms, costs, or other potentially consequential decisions, we use greater caution, prioritize primary sources, and include appropriate limitations. Our content remains general information and does not replace professional advice or the current terms of the relevant provider.
Comparisons, reviews, and recommendations
Comparisons should identify the products or model versions being compared, the criteria used, and the date or context in which the comparison is valid. We aim to compare like with like and avoid conclusions that extend beyond the available evidence.
When an article discusses advantages and disadvantages, we aim to include meaningful limitations rather than presenting marketing claims as independent conclusions. Provider claims are identified as such when the distinction matters.
Recommendations are editorial judgments based on the intended use case, available evidence, and the limitations described in the article. They are not guarantees that a product will be appropriate for every reader.
Code, API examples, prompts, and technical instructions
Technical content is checked against current official documentation where reasonably possible. We aim to identify required dependencies, model names, endpoints, parameters, account requirements, and important security considerations.
Unless an article explicitly states that code was executed and describes the environment, code examples should be treated as documentation-based examples that require reader testing. A second editor reviews technical material, but publication does not guarantee that an example is production-ready, secure, compatible with every environment, or free of errors.
We do not intentionally publish real passwords, private keys, access tokens, personal information, or active API credentials. Example secrets should be clearly recognizable placeholders.
Prompts are presented as adaptable examples. We do not guarantee identical outputs, because results can vary by model, version, settings, context, account, connected tools, and time.
Pricing, plans, limits, policies, and availability
Pricing, plan names, token rates, usage limits, credits, regional availability, product access, and policy terms can change quickly. When these details are important to an article, we aim to cite the relevant official page and include a review or verification date where useful.
We distinguish among free access, free trials, promotional access, usage credits, and paid plans. We avoid describing a product as “free” when payment details, limited credits, a temporary promotion, or another material condition applies.
Readers should confirm current prices, limits, taxes, billing rules, and account availability directly with the provider before purchasing or using a paid service.
Headlines, search optimization, and metadata
We use search engine optimization to help readers discover relevant content, but SEO does not justify misleading titles, invented facts, unnecessary repetition, or content created without reader value.
Titles and descriptions should accurately represent the page. We avoid false urgency, unsupported release dates, exaggerated promises, deceptive “official” wording, and clickbait that withholds the central answer.
We do not change a date merely to make unchanged content appear recent. Publication, update, and review dates should reflect meaningful editorial activity.
Images, screenshots, charts, and visual evidence
Images and screenshots should support the article rather than imply evidence they do not provide. When context matters, we aim to identify the source, relevant interface, and capture date or product version.
Screenshots may become outdated after a product update. They should not be treated as proof that a feature remains available to every account, plan, device, or region.
Illustrations, mockups, or AI-generated visuals must not be presented as genuine screenshots, test results, or official product interfaces. We avoid visual edits that materially misrepresent the underlying evidence.
Alternative text and captions are written to improve understanding and accessibility, not to insert unrelated keywords or unsupported claims.
Content updates and review dates
We do not promise a fixed review schedule for every page. We update content when we identify a meaningful error, receive credible correction information, or become aware of a relevant change to a product, model, interface, price, limit, policy, or official source.
High-traffic or time-sensitive pages may receive more frequent attention, but a displayed review date is not a guarantee that every external detail remains unchanged after that date.
When an update is substantive, we may revise the text, replace links or screenshots, add context, change a recommendation, or remove information that is no longer supportable. Minor spelling, grammar, formatting, and link-maintenance changes may not receive a new review date.
Corrections policy
Readers can report a suspected error by emailing [email protected]. A useful correction request should include the page URL, the statement at issue, an explanation of the concern, and a reliable supporting source where available.
We review correction requests in good faith. We may correct the article, add clarification, narrow a claim, update a source, remove unsupported material, or determine that no change is necessary.
Material factual corrections are not intentionally hidden. When a correction significantly changes the meaning, recommendation, or reliability of a page, we aim to note the correction or explain the material change and update the review date. Minor copy edits, formatting fixes, and non-substantive clarifications may be made without a public correction note.
A product update is not necessarily an editorial error. When accurate information later becomes outdated because a provider changes its product, we generally treat the revision as an update rather than a correction.
Reader feedback and submissions
We welcome feedback, source suggestions, broken-link reports, and correction requests. Feedback may influence future coverage, but it does not guarantee a response, publication, attribution, or a particular editorial outcome.
Submitting a suggestion does not give the sender control over our coverage. We may independently verify the information, edit it, decline it, or use it only as a starting point for further research.
Do not send confidential information, passwords, API keys, private documents, personal data, embargoed material, or information you are not authorized to disclose.
Advertising and commercial independence
Deep-AI.chat may display third-party advertising, including advertising served through Google AdSense. Advertising placement does not determine our factual conclusions, comparison outcomes, criticism, corrections, or topic coverage.
Advertisers do not review or approve our editorial content before publication. The appearance of an advertisement does not mean that Deep-AI.chat recommends or endorses the advertiser, product, service, or claim.
As of the “Last updated” date above, Deep-AI.chat does not use affiliate links and does not publish sponsored content. If we introduce affiliate relationships, sponsorships, paid placements, free products, or another material commercial relationship, we will clearly disclose the relationship in or near the affected content.
We do not accept payment in exchange for a guaranteed positive review, a predetermined comparison result, the removal of accurate criticism, or the concealment of a material conflict of interest.
Conflicts of interest
Editors should disclose to the team any financial, professional, or personal interest that could reasonably affect—or appear to affect—their judgment about a subject.
When a material conflict exists, we may reassign the article, add a disclosure, obtain additional review, narrow the coverage, or decline to publish. A disclosure does not excuse inaccurate or misleading content.
Intellectual property and responsible attribution
We respect copyrights, trademarks, software licenses, database rights, and other intellectual property. Third-party names, screenshots, quotations, code, documentation, model cards, and research remain subject to the rights and licenses of their owners.
We aim to use third-party material only to the extent reasonably necessary for reporting, education, commentary, comparison, or instruction, and to provide attribution where appropriate.
Rights holders can contact [email protected] with the affected URL, a clear description of the material, and evidence of ownership or authority. Additional complaint requirements appear in our Terms of Service.
Practices we do not permit
Our editorial standards prohibit:
- fabricated facts, quotations, citations, credentials, authors, interviews, tests, or user experiences;
- presenting AI-generated text as independent evidence or an authoritative source;
- claiming hands-on testing that did not occur;
- copying or lightly rewriting another source without meaningful original value;
- publishing rumors as confirmed facts;
- inventing release dates, prices, limits, product names, or availability;
- using misleading headlines, manipulated evidence, or undisclosed material commercial influence;
- changing dates solely to create a false impression of freshness; or
- publishing content primarily to manipulate search rankings without providing useful value to readers.
Changes to this Editorial Policy
We may update this Editorial Policy as our workflow, content formats, technology, commercial relationships, or editorial standards change. The revised version will be posted on this page with a new “Last updated” date.
Contact the editorial team
For corrections, source suggestions, editorial questions, or concerns about this policy, contact:
Deep-AI.chat Editorial Team
Website: https://deep-ai.chat/
Email: [email protected]