Data Lineage For LLM Applications

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).