1) Choose sources and what to include or exclude
Pick a small, well‑scoped set of sources you can manage in one afternoon: a CRM segment (e.g. active customers last 24 months, support cases), a single shared folder in Google Drive or OneDrive, and the key shared spreadsheets that drive decisions (pricing, SLAs, product lists).
Operational rules: include customer-facing notes, resolved support tickets, product/spec summaries and meeting notes; exclude invoices, contract PDFs, payroll, legal correspondence and any field that contains full payment, NI or passport numbers. For CRMs (HubSpot or Salesforce) prefer structured fields and resolved case summaries rather than raw activity logs; for Marketo/Pardot include campaign-level summaries, not raw click logs.
Aim for a corpus you can explain in plain language to the team: “these records plus these folders, excluding X and Y”. That clarity helps with consent, audits and quick removals if needed.
2) Strip, redact, summarise and chunk for efficient retrieval
Start with three simple passes: remove and redact PII, summarise long documents, then chunk into retrieval‑friendly pieces. Redaction rules for a small team: remove full bank/account numbers, full national identifiers, and replace emails/phones with tokens (e.g. [EMAIL], [PHONE]). Keep safe identifiers like company names and product codes if they’re necessary for context.
To limit size and token costs, summarise long attachments to one‑paragraph abstracts and drop boilerplate (email signatures, repeated legal headers). Deduplicate identical notes and merge very short, related rows from spreadsheets into one context block.
Chunking: make chunks short and self‑contained — one to three paragraphs each (a practical rule of thumb: 200–400 tokens per chunk). For spreadsheets, create a natural language sentence per row or group of rows so the retriever can match queries easily. Index metadata with a few fields (source, record_id, date, redaction_level) so you can trace any returned snippet back to origin.
3) Add provenance, guardrails and a quick test plan
Add minimal provenance fields you can populate in an afternoon: source_system (CRM/Drive/Sheet), record_id, author (or team), date_added, redaction_level and consent_flag. These fields power safe answers: if the provenance shows a redacted or low‑trust source, the workflow can escalate to human review.
Guardrails: limit the initial corpus size (for example, start with 1,000–2,000 chunks), enforce role‑based access to the index and never allow the RAG system to expose raw excluded categories (payments, legal). Route any generated answer that cites records with redaction_level > 1 to a human reviewer before it reaches a customer.
Quick afternoon test plan: 1) pick 30 representative queries your team cares about; 2) run RAG against the corpus and capture answers plus provenance; 3) compare results to a small gold set (correct answers or ‘no answer’); 4) flag hallucinations and cases where provenance is missing or too vague. If >10% answers are unsafe or incorrect, tighten redaction, reduce chunk size or add more accurate summaries.
If you want a short, practical run‑through with templates and a hands‑on checklist, Optira can help review your plan and a small pilot without heavy tooling.