Data Lineage For LLM Applications

Kreatebots lineage specification for prompt management, data provenance, human review, content approval, distribution tracking, and user feedback. Ensure full visibility, governance, and analytics across AI-generated content workflows.


Here is how we manage lineage for LLM based applications

1. Input Data Provenance

  • Right now you track the prompt but not enough detail about the data source.

  • Add attributes for:

    • data_source_id (UUID)
    • type (document_upload, database, API, web_scrape)
    • location (bucket path, URL, dataset ref)
    • retrieved_at (timestamp)

  • Relationship: Prompt → Data Source(s) → Model Run.

👉 This closes the gap for compliance: “Where did this fact come from?”

2. Transformation Metadata

  • You have Model Run and Output, but you don’t track the transformations.

  • Add:

    • transformation_id
    • operation (embedding, summarization, classification, rewriting, clustering)
    • parameters (chunk size, embedding dimension, etc.)

  • Relationship: Input → Transformation → Output.

👉 Useful if you later reprocess with different configs and need comparability.

3. Prompt Management Lifecycle

  • Right now you have versioned prompts but not governance around them.

  • Add:

    • prompt_status (draft, testing, active, deprecated)
    • experiment_group (A/B testing for prompts)
    • approval_workflow (who signed off the prompt).

👉 This prevents stale or experimental prompts from slipping into production.

4. Human Interaction Granularity

  • You track reviews, but not roles or workflow steps.

  • Add:

    • reviewer_role (subject_expert, editor, compliance_officer)
    • review_comments (structured feedback, not just “approved/rejected”).
    • edit_type (minor text edit, factual correction, compliance removal).

👉 This makes your lineage richer for audit and quality metrics.

5. Distribution Enrichment

  • You track page URLs + impressions, but you could also log:

    • channel (portal, API, mobile_app, email)
    • audience_segment (internal, external, premium users, geography).
    • campaign_id (if content is tied to marketing).

👉 Gives analytics on which distribution channel drives adoption.

6. User Feedback Loop

  • You track feedback as good/bad, but could expand to:

    • feedback_category (accuracy, clarity, bias, irrelevant).
    • feedback_confidence (rating 1–5, not just binary).
    • follow_up_action (feedback linked to prompt revision, retraining, or bug report).

👉 Lets you close the loop: user feedback → system improvement.

7. Access & Security Lineage

  • Who accessed or exported lineage data itself?

  • Add audit logs:

    • audit_id
    • actor (system/human)
    • action (viewed_lineage, exported_lineage, deleted_record)
    • timestamp.

👉 Essential if you’re handling regulated content.

8. Time-Based Snapshots

  • Sometimes lineage changes over time (prompt v1 vs v2).
  • Consider supporting temporal lineage queries: “What was the content lineage on Sept 1, 2025?”

👉 Helps with audits, compliance, and debugging.

✅ With these extensions, you’ll have:

  • Upstream coverage (where data came from, how it was transformed).
  • Midstream coverage (prompt lifecycle, human edits with role granularity).
  • Downstream coverage (distribution channels, feedback categories).
  • Governance coverage (audit logs, temporal lineage).


Data-lineage-applications    Data-lineage-automation    Data-lineage-factors    Data-lineage-for-chatbots    Data-lineage-for-content-mana    Data-lineage-for-content-mana    Data-lineage-for-data-product    Data-lineage-for-llm-applicat    Data-lineage-overview    Data-lineage-properties-for-c   

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