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Each listing spells out exactly what's inside — the steps, the ready-to-run code, and which AI tools it works with — so you know what you're getting before you buy.

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Use when a retrieval corpus has exact or near-duplicate docs/chunks inflating embedding cost or polluting top-k results, and you must detect and prune them.

  • 7-step procedure
  • Runnable Python included
  • 7-point quality checklist
dataWorks with any modern AI assistant·6 min read·Updated today

Invoke when building or tuning a RAG/semantic-search pipeline's chunking: pick strategy, chunk size, and overlap validated against retrieval metrics.

  • 9-step procedure
  • Runnable Python included
  • 8-point quality checklist
dataWorks with any modern AI assistant·8 min read·Updated today

Design data governance frameworks: data classification, lineage tracking, access policies, retention, and compliance mapping.

  • 9-step procedure
  • 6 pitfalls to avoid
  • Installs into 6 tools
dataWorks with any modern AI assistant·3 min read·Updated today