learn trust-ai 5-LLM-vs-DIY

Converting descriptions to verbs & nouns

~2 m

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Legacy Metadata

Export from existing systems & ingest into AI assisted cleaner

Normalize Formats

Convert to consistent schema, UTF-8, date formats

Parse and Tokenize

Extract fields such as title, keywords, creators, dates, rights

Initial Validation

Check for missing or corrupt fields

AI-Assisted Enrichment

Fill gaps, suggest tags, generate summaries

Flag for Manual Review

Engineer reviews malformed data

Problems?

Ambiguity Detected? Conflicting tags or unclear references

Human Fix

Human or Expert Resolution: Resolve conflicts and verify context

Consolidate

Consolidate Cleaned Records: Merge AI output with original metadata

Fact Check

AI Hallucination Check: Cross-verify AI suggestions with trusted references

Reject

Reject or Revise AI Output: Engineer adjusts or re-prompts AI

Enhance Search Index: Update catalog and indexing structures

Deploy

Deploy to AI Agent: Provide cleaned metadata for search, cataloguing, workflows

Monitor

Continuous Monitoring: Audit AI queries and metadata usage

Issues?

Issues Detected? Search errors, user feedback, new ambiguities

Iterate

Iterative Improvement: Feed issues back to cleanup pipeline

Steady-State

Operate system until problems are exposed

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  • try something like this….
flowchart TD
    A[Legacy Metadata]
    A --> B[Normalize Formats]
    B --> C[Parse and Tokenize]
    C --> D[Initial Validation]

    D -->|Valid| E[AI-Assisted Enrichment]
    D -->|Invalid| F[Flag for Manual Review]

    E --> G{Problems?}
    G -->|Yes| H[Human Fix]
    G -->|No| I[Consolidate]

    H --> I

    I --> J{Fact Check}
    J -->|Suspected Hallucination| K[Reject]
    J -->|Verified| L[Enhance Search]

    K --> L

    L --> M[Deploy]
    M --> N[Monitor]
    N --> O{Issues?}
    O -->|Yes| P[Iterate]
    O -->|No| Q[Steady-State]