Knowledge & Document Intelligence — Ingestion Playbook

Convert documents into reliable, searchable knowledge: an ingestion playbook

Goal: turn scattered, messy documents into dependable, discoverable context that powers assistants, search, and downstream analytics while avoiding noise, stale data, and accidental exposure of sensitive information.

Why this matters

Poor ingestion creates noisy context, increases hallucinations, surfaces sensitive data, and erodes trust in AI systems. Good ingestion makes knowledge findable, trustworthy, auditable, and actionable. This playbook preserves the original five-step structure while adding concrete actions, owners, acceptance criteria, and tests you can run.

Core steps (expanded)

1. Document inventory & owner mapping

   What to do: build a prioritized catalog of content sources (share drives, CMS, wikis, email archives, databases, PDFs, scanned documents, code repositories, policies, SOPs).

   Who: content owners (team leads), knowledge steward, security officer.

   Outputs: inventory table with fields: SourceID, Owner, Path/URL, File types, Estimated volume, Sensitivity classification (public/internal/confidential), Retention policy, Last-modified, Business use cases.

   Acceptance criteria: inventory covers top 80% of queries and all high-risk sources; each source has an assigned owner and sensitivity label.

2. OCR, cleanup & canonicalization

   What to do: apply OCR to scanned files, normalize formats, remove extraneous headers/footers, fix broken encodings, deduplicate versions, and collapse near-duplicate files into canonical records.

   Tips: prefer high-quality OCR models, keep OCR text & original file both stored, and record OCR confidence scores as metadata. For templates (invoices, reports), extract structured fields into a parallel data store rather than relying solely on full-text search.

   Outputs: cleaned document store, canonical IDs, deduplication mapping.

   Acceptance criteria: OCR text completeness > X% for target docs, no more than Y% duplicate content in initial sample.

3. Chunking & metadata enrichment

   What to do: break long documents into semantically coherent chunks and enrich each chunk with metadata that supports retrieval, filtering, and safe use by assistants.

   Suggested metadata schema (start point):

   • document_id
   • chunk_id
   • title
   • owner
   • source_type (wiki/pdf/email/code)
   • created_date / last_modified
   • version
   • sensitivity_label
   • business_topic / taxonomy tags
   • language
   • ocr_confidence
   • canonical_id (if deduped)

   Chunking rules: prefer semantic chunking by paragraph/section (200–1,200 tokens per chunk depending on model and retrieval approach). For Q&A assistants, 500–800 tokens is a practical target. Preserve headings as metadata so chunks remain interpretable.

   Enrichment: add taxonomy tags, named entities, topic vectors, and optional summary or extractive highlights. Record provenance fields to allow tracebacks from answers to original sources.

4. Indexing and access controls

   What to do: load vector and optionally keyword indexes into your retrieval system, and enforce access controls at the chunk or document level. Map sensitivity labels to policies (block, mask, require MFA, log access).

   Choices to make: hybrid retrieval (dense vectors + sparse keyword indexes) often gives best results. Decide on embedding model, chunk embedding cadence (on-change vs nightly), and versioning for retraining/rolling back embeddings.

   Security & privacy checklist: ensure PII is identified and either redacted or treated by strict access rules; log retrievals; limit context window for external-facing assistants; run prompt-injection filters.

5. Evaluation checklist for retrieval quality & safety

   What to do: test retrievals with representative queries, measure quality, and iterate.

   Suggested tests & metrics:

   • Relevance test: For a set of 50 representative queries, measure whether returned chunks contain the correct facts (binary pass/fail) and compute precision_at_k.
   • Answer source traceability: For assistant responses, verify the top sources are correctly cited and traceable to document_id and chunk_id.
   • Hallucination check: Rate answers against ground-truth; track hallucination rate over time.
   • Sensitivity leakage test: Run queries designed to surface PII to confirm masking/redaction policies hold.
   • Staleness/freshness check: Confirm that recently updated authoritative content supersedes older guidance in retrieval ranking.
   • Performance: indexing time, query latency, and embedding pipeline throughput.

   Failure criteria and rollback: define thresholds for acceptable precision and hallucination rate and an automated rollback plan if thresholds are breached during rollout.

Practical roll-out blueprint

1. Start with a pilot: choose one business domain with clear owners and 500–5,000 documents.
2. Run manual relevance tests and sensitivity tests with the business owner and security reviewer.
3. Iterate chunking, metadata, and ranking using feedback from 10–20 real queries.
4. Expand incrementally to other content areas, preserving canonicalization rules and reuse of taxonomy mappings.
5. Monitor key metrics weekly for the first 90 days, then monthly after stabilization.

Quick decision guide & common trade-offs

If you must choose:

• Prioritize accurate provenance and sensitivity filtering over maximal coverage.
• Prefer smaller, semantically coherent chunks for assistants; prefer larger chunks for document-level analytics.
• Use hybrid retrieval for mixed technical and natural-language queries.

Evaluation checklist (copyable)

1. Inventory completed with owners and sensitivity labels for primary sources.
2. OCR applied where needed; OCR confidence stored.
3. Deduplication and canonical IDs assigned.
4. Chunks created, with taxonomy and provenance metadata on each chunk.
5. Indexing pipeline in place (embedding model noted) and access controls enforced.
6. Representative test queries executed; precision_at_k and hallucination rate recorded.
7. Sensitivity leakage tests passed.
8. Monitoring and rollback plan established.

Example metadata snippet (JSON-like)

{
  "document_id": "HR-employee-handbook-v3",
  "chunk_id": "HR-employee-handbook-v3-chunk-12",
  "title": "Leave policies",
  "owner": "HR",
  "source_type": "pdf",
  "created_date": "2024-02-01",
  "sensitivity_label": "internal",
  "taxonomy": ["HR","leave","benefits"],
  "ocr_confidence": 0.96
}

Next improvements and capability opportunities

Consider converting the evaluation checklist and inventory into interactive forms so teams can record results, ownership, and remediation actions. Track ingestion outcomes over time (precision, hallucination rate, PII incidents) and make dashboards to guide continuous improvement.

References & shortcuts

• Start pilot with a narrowly scoped domain where owners are cooperative.
• Log all retrievals and corrections to build a feedback loop for re-ranking and re-embedding.

Use this playbook as a living instrument: preserve owners, run the evaluation checklist regularly, and treat ingestion as an ongoing practice rather than a one-time project.