Copyright & Important Notice

Acknowledgment of Conditions

By using, adapting, implementing, or distributing DCPF or DCPF-based materials, the user acknowledges the copyright, permissions, responsibilities, disclaimers, and limitations stated on this page and accepts responsibility for their own implementation and use.

Please read this entire page before applying DCPF.

Copyright

This website and the DCPF Introductory Edition, including their original text, explanations, diagrams, visual elements, and protectable selection and arrangement, are protected under applicable copyright law. Except as expressly permitted here or by applicable law, protected material may not be reproduced, republished, transmitted, incorporated into another publication, course, template collection, or commercial product, or otherwise used without prior written permission from the copyright owner.

DCPF and the Distributed Cognitive Prompting Framework for AI Collaboration identify the organizational framework described in these materials. This notice does not claim copyright in ideas, facts, systems, processes, methods of operation, or other material excluded from copyright protection under applicable law. Copyright protects the protectable expression embodied in the materials.

Free Distribution Permission for the Ebook

The official DCPF Introductory Edition ebook is made available by the author free of charge. Readers may download, retain, reproduce, and share complete, unmodified copies for personal, educational, professional, and organizational use, provided that all copyright, attribution, legal and use notices, and identifying information remain intact.

This permission does not authorize anyone to publish or distribute an altered edition as an official DCPF publication; sell, license, or commercially redistribute the ebook itself; remove or obscure copyright, attribution, or source information; or use the DCPF name or the author’s identity in a way that suggests certification, sponsorship, endorsement, partnership, or affiliation.

dcpfai.org is the authoritative site and definitive reference for the most current official ebook editions, DCPF materials, permissions information, and updates.

Limited Template License

Readers who lawfully obtain the publication may reproduce, complete, modify, and use templates expressly identified as copyable for:

personal learning and experimentation;
educational and training activities; and
internal organizational testing, evaluation, and experimentation.

Template Restrictions

This permission applies only to templates expressly identified as copyable. It does not authorize a user to:

reproduce or distribute individual chapters or substantial portions of the book as separate publications, except as permitted by law or written permission;
reproduce or separately distribute diagrams or other visual elements except as allowed by law or written permission;
publish, sell, license, or redistribute blank or substantially unchanged DCPF templates as a standalone product, template library, training package, or competing publication;
remove or obscure copyright or attribution notices; or
suggest certification, sponsorship, endorsement, partnership, or affiliation through use of the DCPF name or author’s identity.

DCPF Is an Organizational Framework

DCPF is an experimental, experience-based organizational architecture for structuring collections of AI interactions through Governance, Research, Knowledge, and Runtime. It is not an AI model, software platform, autonomous agent, or substitute for human judgment. It does not operate, control, retrain, or alter an AI system or override that system’s limitations, safeguards, context limits, service conditions, or provider policies.

DCPF can be understood like a structured form. The form supplies fields, categories, and an organizational sequence, but the person completing it determines what information enters those fields and how it is used.

It can also be understood like a filing cabinet. The cabinet supplies drawers, folders, labels, and structure, but it does not author, verify, approve, or own the contents placed inside it. DCPF likewise organizes project information without taking ownership or responsibility for that information.

Subject to applicable law and the rights of others, users retain ownership of content they author and remain responsible for all content they create, select, adapt, or incorporate.

Experimental Status and Validation Boundary

DCPF has demonstrated promise through the development work, applications, and practicums presented in the Introductory Edition, but it has not been broadly or independently validated across domains, organizations, AI systems, or production environments.

Users should begin in a controlled, low-risk sandbox or testing environment and validate their own implementation against the requirements of their domain, project, AI system, and operating environment. Any decision to deploy an experimental DCPF implementation outside testing remains the responsibility of the user or organization making that decision.

No Guarantee of Results

The author does not warrant or guarantee that DCPF or any example, template, instruction, workflow, or practicum will produce any particular result. DCPF cannot guarantee the accuracy, completeness, currency, reliability, objectivity, safety, usefulness, or suitability of AI-generated output, or identical results across models, platforms, accounts, sessions, projects, or users.

Validation provides evidence about performance under tested conditions. It does not prove perfect, universal, or continuously consistent performance. AI systems may generate inaccurate, incomplete, outdated, biased, inconsistent, or misleading material and may omit important context. Organization can make collaboration more structured and reviewable, but it cannot eliminate these risks.

User Control and Responsibility

The user remains in full control of how DCPF is applied and is responsible for:

selecting AI systems, tools, data sources, and project purposes;
deciding what information may lawfully and appropriately be submitted to an AI service;
creating, modifying, testing, documenting, and maintaining DCPF artifacts;
independently verifying material statements, recommendations, calculations, citations, and conclusions;
deciding whether an output is accepted, revised, rejected, published, shared, or acted upon;
protecting confidential, personal, proprietary, and security-sensitive information;
evaluating intellectual-property, privacy, security, bias, fairness, transparency, and accessibility concerns; and
complying with applicable laws, regulations, professional obligations, contracts, and organizational policies.

Use of DCPF does not transfer responsibility for the user’s decisions, actions, adaptations, or resulting work products to DCPF, the author, the publisher, or an AI provider.

Assumption of Risk and Third-Party Use

Each user assumes the risks associated with designing, configuring, customizing, testing, validating, maintaining, and using their own DCPF implementation. This applies to personal, educational, organizational, professional, commercial, and client-facing use.

The author and publisher do not control project information or instructions; selected AI models, platforms, software, sources, or tools; customized artifacts; accepted or shared outputs; validation or professional review; or resulting decisions, services, deliverables, and work products. The user remains responsible for determining whether these items are accurate, lawful, safe, appropriate, and suitable for their intended purpose.

Use of DCPF is undertaken at the user’s own risk. To the fullest extent permitted by applicable law, the author and publisher are not responsible or liable for losses, damages, claims, liabilities, costs, business interruption, lost revenue, lost data, consequential losses, or other harm arising from or related to a user’s selection, customization, implementation, use, misuse, interpretation, or reliance upon DCPF, a DCPF artifact, an AI system, an AI-generated output, or a resulting work product.

This limitation applies to harm experienced by the user and to claims, losses, or damages involving clients, customers, employers, organizations, project stakeholders, or other third parties. Professional or organizational users remain responsible for appropriate contracts, review, testing, validation, qualified oversight, insurance, security controls, and other safeguards for their activities.

Nothing here excludes or limits any responsibility or liability that applicable law does not permit to be excluded or limited.

Third-Party AI Systems

The author does not operate, control, or guarantee third-party AI systems. Providers may apply different limits, safeguards, data-handling practices, privacy terms, intellectual-property terms, retention practices, availability conditions, and usage policies.

Users are responsible for understanding the terms and risks of their selected systems and for deciding what information is appropriate to supply. The ability to organize or transfer DCPF materials across systems does not mean every system will interpret, retain, retrieve, or execute them in the same way. Validate important work in the environment where it will be used.

Professional and High-Stakes Uses

DCPF materials are provided for educational and informational purposes. They are not legal, medical, financial, accounting, cybersecurity, engineering, employment, regulatory, or other professional advice.

AI-generated material concerning health, law, finance, public safety, employment, compliance, security, or another consequential matter should be independently reviewed by appropriately qualified professionals and checked against current primary or authoritative sources before use in a decision.

Use of DCPF does not create a professional, advisory, fiduciary, or other special relationship between a user and the author or publisher.

Changes and Current Materials

AI models, platform features, provider terms, governance practices, laws, and regulations continue to change. Examples may behave differently in another environment or at a later date. Users are responsible for testing materials in their current environment and revising implementations when evidence, technology, law, policy, or project requirements change.

DCPF materials may also be revised as the framework and supporting resources evolve. Copies obtained elsewhere may be older or superseded. Consult dcpfai.org to confirm the current official edition and materials.

Disclaimer of Warranties and Limitation of Responsibility

To the fullest extent permitted by applicable law, DCPF publications, website content, and materials are provided for educational and informational purposes on an “as is” and “as available” basis. The author and publisher make no express or implied warranty that use of DCPF will be error-free, uninterrupted, suitable for a particular purpose, or capable of producing a particular outcome.

To the fullest extent permitted by applicable law, the author and publisher are not responsible for losses, damages, claims, costs, liabilities, or consequences arising from selection, customization, implementation, use, misuse, interpretation, or reliance on DCPF, a DCPF artifact, an AI system, an AI-generated output, or a resulting work product, including third-party claims.

DCPF remains a human-directed organizational framework. Human judgment, verification, and accountability remain essential at every stage.

Fair Use, Third-Party Materials, and Permissions

Nothing on this page limits fair use or another exception or limitation provided by applicable law. Fair use depends on the facts and circumstances of each use. Attribution alone does not replace permission when permission is legally required.

Third-party trademarks, service marks, product and platform names, company names, quotations, and other third-party materials remain the property of their owners. Their appearance does not imply sponsorship, endorsement, or affiliation.

Requests beyond the permissions expressly granted here may be submitted through the forthcoming Permissions & Contact page.