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AI SDLC Artifact Tracking ER Diagrams

Need to map how AI development artifacts connect? These AI SDLC artifact tracking ER diagrams show how to model entities like requirements, prompts, model versions, datasets, and audit records. They're perfect for ML engineers, data scientists, and compliance teams who need clear traceability across the AI lifecycle.

Standard Chen 1976 / Everest 1976 (crow's foot)Engine schematex-erdExport SVG · PNG · PDF
3 templates
ERD AI SDLC Artifact Tracking ER
AI SDLC Artifact, Prompt, and Model Version Traceability ER DiagramAI SDLC Artifact, Prompt, and Model Version Traceability ER DiagramAI SDLC Artifact Tracking ERUse template →AI SDLC Artifact and Requirement Traceability ER DiagramAI SDLC Artifact and Requirement Traceability ER DiagramAI SDLC Artifact Tracking ERUse template →Healthcare AI Regulatory Submission Artifact and Audit Trail ER DiagramHealthcare AI Regulatory Submission Artifact and Audit Trail ER DiagramAI SDLC Artifact Tracking ERUse template →

Nothing close enough? Start from a blank erd → Describe it in one paragraph.

How to

How to use an erd template.

  1. 01Identify core entities

    List all artifact types you need to track: requirements, user stories, prompts, model versions, datasets, audit logs, and any domain-specific items.

  2. 02Define relationships

    Map how artifacts link—for example, a requirement may inform a prompt, a prompt produces a model version, and an audit record tracks any change.

  3. 03Set cardinalities

    Determine one-to-many or many-to-many relationships—e.g., one model version can be associated with many prompts, but each prompt links to exactly one version.

  4. 04Add attributes and keys

    For each entity, include primary keys and necessary fields like timestamps, version numbers, status, or compliance tags.

  5. 05Generate and refine

    Use the ER diagram maker to generate a starting diagram from a text description, then adjust layout, notation, and granularity as needed.

FAQ

Questions about erd templates

What entities should I include in an AI SDLC artifact tracking ER diagram?

Typical entities include requirements, prompts, model versions, datasets, test results, audit records, and deployment artifacts. Tailor the entity set to your specific AI development workflow.

How do I represent model versions and prompt versions in an ER diagram?

Model versions and prompt versions are usually separate entities with a many-to-many relationship (a model version may be produced by multiple prompts, and a prompt can lead to multiple model versions). Include version numbers and timestamps as attributes.

Can I track audit trails for compliance in an ER diagram?

Yes, add an audit record entity linked to any artifact that changes. Include fields like changed_by, change_type, timestamp, and approval status to satisfy governance and compliance requirements.

How do I generate an ER diagram from my AI project data?

Use our ER diagram maker: describe your entity types and relationships in plain language, and the tool will generate a starter diagram. You can then import data or manually refine the diagram in the editor.

What is the difference between an artifact and a requirement in AI SDLC?

A requirement is a specific need or constraint for the AI system, while an artifact is any tangible output of the development process (e.g., a prompt, model, dataset, or report). Requirements often link to artifacts via traceability relationships.