Why a Static FAQ Won’t Cut It
Assistants fail when they quote stale docs or can’t find the latest price list. Retrieval-augmented generation (RAG) fixes this by pulling from an authoritative, up-to-date knowledge base before answering. The catch: RAG is only as good as the content and indexing you feed it.
Start with a “Source of Truth” Folder Taxonomy
- /Products — one-pagers, specs, images
- /Pricing — current price list + dated archive
- /Policies — shipping, refunds, privacy
- /Playbooks — support macros, how-tos
- /Training — glossary, tone, examples
Rules: one topic per file; clear filenames (e.g., pricing_2025-Q3.pdf); front-matter with title, version, effective date, and owner.
Add Metadata the Model Can Use
- category: pricing | policy | product
- effective_from / effective_to
- locale: en-US | ar-AE
- visibility: public | internal
- canonical_url (if published)
This helps retrieval prioritize the right document when there are conflicts.
Chunking That Respects Meaning
Index sections, not whole PDFs. Good chunk sizes: ~300–800 tokens with small overlaps. Split on headings so answers keep context.
Sync That Actually Self-Updates
Pick a sync path (Drive, SharePoint, Notion) and run a scheduled job that:
- Detects new/changed files
- Extracts text + metadata
- Chunks and re-embeds only what changed
- Updates the index and invalidates stale entries
Incremental indexing keeps costs low and freshness high.
Versioning & “What’s Effective Now”
Keep one live version per topic; archive the rest. Use effective_from to resolve which version answers a question today. If a query asks about last year’s policy, retrieval can include archived chunks.
Guardrails: Governance over Guesses
- Provenance in answers: show title, version, and source link.
- Redaction rules: exclude secrets (API keys, PII) from indexing.
- Locales: keep English and Arabic separate unless mixed-language retrieval is validated.
- Human review loop: log unanswered/low-confidence questions → create or fix content → re-index.
Example: 10-Day Build Plan
- Days 1–2: audit docs; create taxonomy; define metadata keys.
- Days 3–4: clean and split top-10 FAQs into single-topic one-pagers.
- Day 5: stand up the index; test chunking on pricing & policy.
- Week 2: wire sync; add provenance to answers; run a weekly “content clinic”.
KPIs for a Living Knowledge Base
- Coverage — % of top questions answerable with high confidence
- Freshness lag — time from doc update → index update
- Deflection rate — % resolved without human help
- Edit velocity — content fixes shipped per week
- Accuracy audits — spot-check answers with source links
Common Pitfalls (and Quick Fixes)
- Monolithic PDFs → explode into one-topic files with headings.
- Stale pricing → make pricing its own folder with owners & expiry dates.
- Index bloat → archive aggressively; keep only live docs in the primary index.
- No provenance → add source cards; they build trust and speed debugging.
Why This Matters Now
RAG isn’t a magic wand — it’s a discipline. Teams that invest in document hygiene, metadata, and incremental indexing report far more reliable assistants than teams that “just vectorize everything.” That’s the difference between a demo and a durable system.