Knowledge Framework and Knowledge Repository
Preserving Accepted Knowledge as a maintained foundation for future work.
What Is the Knowledge Framework?
The DCPF Knowledge Framework owns the architectural responsibility for preserving Accepted Knowledge so it can be maintained and reused across sustained AI-assisted work. It defines how approved information is selected, represented, organized, made accessible, and kept current.
The Knowledge Repository is the persistent representation and maintained physical project home of that Accepted Knowledge. It may be a document, folder system, database, wiki, content repository, or another implementation appropriate to the project.
DCPF is experimental.
DCPF has demonstrated promise through development work, practical applications, and training practicums, but it has not been broadly or independently validated. Begin with controlled, low-risk Knowledge practices, validate them for your domain and environment, independently verify consequential information, and retain human authority over acceptance and use.
Accepted Knowledge Has a Deliberate Status
Accepted Knowledge is information the project is prepared to rely upon after the applicable human review or authorization. It is not simply material that an AI found, generated, summarized, or stored.
The repository can preserve facts, findings, qualifications, uncertainty, terminology, decisions, procedures, standards, examples, source records, and other reusable project intelligence. Acceptance does not mean permanent certainty. Accepted Knowledge may remain qualified, limited in scope, or subject to future review.
The Five Standard Knowledge Responsibilities
Every DCPF implementation should address five standard Knowledge responsibilities. Their form and depth should match the scale, risk, and maintenance needs of the work.
1. Acceptance and Knowledge Status
Define what information is accepted, who authorized it, the scope of that acceptance, and how accepted, provisional, unresolved, excluded, superseded, or retired material is distinguished.
Teaching question: What is the project prepared to rely upon, and how can a user tell what status each item has?
2. Knowledge Representation
Preserve the content, qualifications, definitions, relationships, procedures, examples, and source basis needed for the Knowledge to remain understandable and usable.
Teaching question: What must be represented so future users and AI systems interpret this Knowledge correctly?
3. Organization and Retrieval
Give Accepted Knowledge a maintained structure, location, naming approach, and retrieval method suited to the project.
Teaching question: Where does this Knowledge belong, and how will people or authorized systems find the correct material?
4. Access and Use
Define who or what may access the Knowledge, which portions are relevant to particular work, and how complete approved Knowledge is supplied to Runtime.
Teaching question: Who may use this Knowledge, for which purposes, and how will the executing AI actually receive it?
5. Maintenance and Change Control
Define how Knowledge is reviewed, corrected, versioned, replaced, retired, and protected from unauthorized changes.
Teaching question: How will the project keep its Knowledge current without allowing drafts or AI suggestions to become authoritative automatically?
Two Paths Into Accepted Knowledge
Research-to-Knowledge Path
A Research Output remains evidence until an authorized person reviews it. The reviewer may accept all, some, or none of the findings. Only the accepted material moves into the Knowledge Repository with its necessary context, qualifications, and source basis.
Direct-Acceptance Path
Some material is already authoritative within the project and does not require unnecessary new Research. Examples may include a policy approved by the organization, a specification supplied by its owner, a client-authorized requirement, or an existing canonical project record. An authorized person may accept that material directly into the Knowledge Repository when its authority, relevance, and applicable Governance are established.
Direct acceptance is not a shortcut for weak evidence. It recognizes that Research is unnecessary when the project already possesses authoritative material and the authorized human can establish its status.
Metadata Can Help, but It Is Supplemental
Metadata such as title, identifier, version, owner, acceptance date, review date, source, status, or applicable scope can improve retrieval and maintenance. DCPF does not require every implementation to use an elaborate metadata system.
Use metadata when it makes status, provenance, retrieval, maintenance, or appropriate use clearer. Metadata supports the five standard responsibilities; it is not a separate substitute for them.
Human and AI Maintenance Boundaries
AI can help organize, compare, format, tag, summarize, identify possible conflicts, and draft proposed updates. It may also help evaluate whether stored Knowledge appears incomplete or outdated.
AI does not independently authorize its own additions, corrections, replacements, or deletions. A generated statement does not become Accepted Knowledge because it sounds confident, appears in a repository draft, or was used successfully once. The authorized human or project process determines whether a proposed change becomes authoritative.
- Keep proposed changes distinguishable from current Accepted Knowledge.
- Require the applicable review before changing authoritative status.
- Preserve qualifications and unresolved conflicts rather than erasing them for simplicity.
- Retain enough version information to identify which Knowledge governed earlier work when that history matters.
Runtime Must Receive the Complete Approved Knowledge It Requires
Runtime cannot rely on Knowledge it cannot access. A file name, repository path, link, or reference to another conversation is not enough unless the AI system can actually retrieve and read that material.
The Runtime artifact must supply or provide verified access to the complete approved Knowledge required for the current task. If required Knowledge is unavailable, incomplete, conflicting, or outside the AI’s access, Runtime should stop and identify the gap rather than inventing project facts or pretending the material was used.
When a Knowledge Responsibility Is Left Undefined
Undefined status can blur approved and unapproved material. Weak representation can strip away qualifications or meaning. Poor organization can cause the wrong version to be retrieved. Undefined access can leave Runtime without required Knowledge. Missing maintenance controls can allow stale information, accidental changes, or AI-generated assumptions and hallucinations to persist as though they were authoritative.
The Knowledge Framework makes these responsibilities visible so the project can preserve not only information, but also the trust conditions needed for appropriate reuse.
Use the Smallest Repository That Works
A personal project may maintain Accepted Knowledge in one well-structured document. A larger effort may need folders, version control, access permissions, review schedules, databases, or specialized repository tools. DCPF requires a dependable maintained home, not a particular technology.