Blog Post

TL;DR

Most GenAI pilots stall because they’re built on untrusted data and loose access controls. The path that works:

1. Start with one workflow (e.g., policy Q&A, support triage, sales enablement).
2. Use RAG to ground answers in your documents—not model guesswork.
3. Wrap guardrails (SSO/MFA, data boundaries, red-teaming, logging).
4. Measure outcomes (accuracy, time saved, escalations, user satisfaction).
5. Scale cautiously—small surfaces, clear owners, versioned prompts.

What RAG is (and isn’t)

• Is: Search + context → the model. Your content powers the answer.
• Isn’t: A silver bullet for bad or missing data. You still need quality, permissions, and lineage.

Architecture in plain English:

Documents → chunk & embed → vector search → retrieve top context → build a prompt → model answers → log sources + feedback.

7 use cases that ship fast

1. Policy/IT/HR assistant — Employees ask, bot cites sources and links.
2. Support triage — Suggest responses with article snippets; agent approves.
3. Sales enablement — Collateral finder with customer-safe text.
4. Procurement & legal drafting — First-pass clauses from templates + policy.
5. Engineering runbooks — “How do I…?” against ops docs; links to steps.
6. Data explainer — Translate metric definitions and query examples.
7. Compliance Q&A — Map questions to ISO/NIST/PDPL controls with citations.

Pick one, tie it to a KPI (handle time, tickets deflected, draft speed, accuracy), and ship.

Guardrails you actually need

Identity & access

• SSO + MFA; respect document permissions on retrieval; mask PII where required.

Data boundary

• Keep prompts/outputs inside your tenant; disable copy to public tools.

Filtering & safety

• Strip secrets, PII, or out-of-scope content from retrieved chunks.
• Prompt injection defenses (limit external links, sanitize inputs).

Logging & evidence

• Store prompts, retrieved doc IDs, and model outputs with timestamps.
• Feedback loop (👍/👎 + reason) tied to the retrieved sources.

Human in the loop

• Draft → review → send for external comms; auto-approve only where risk is low.

Data & evaluation

• Chunking strategy: titles + headings; keep semantic units intact.
• Index hygiene: dedupe, expire stale content, version embeddings.
• Accuracy: judge with 50–100 real questions; require citations.
• Latency/cost: cache frequent questions; prefer smaller models if quality holds.
• Drift: monitor answer quality weekly; re-index when docs change.

30-60-90 rollout

Days 1–30 —

Prototype with permissions

• Pick one workflow + KPI; wire SSO; index a small, governed content set.
• Ship a closed beta to 10–20 users; collect feedback + errors.

Evidence: accuracy ≥ 80% on test set; citation rate ≥ 95%; PII leakage 0.

Days 31–60 —

Harden & measure

• Add PI/secret filters; tighten retrieval (rerankers); set logging & reviews.
• Publish weekly metrics: accuracy, handle time, deflection, escalations.

Evidence: ticket deflection ≥ 15% or draft time −30% with quality steady.

Days 61–90 —

Scale carefully

• Add a second content source; define owners; automate re-index on doc changes.
• Tabletop test prompt-injection and data-exfil scenarios; improve rules.

Evidence: stable accuracy; no policy breaches; users opt-in grows.

Tech pattern (vendor-neutral)

• Store & search: vector DB (pgvector, Pinecone, Weaviate) + metadata filter
• Index: chunker + embedder (Open source or provider embeddings)
• Rerank: cross-encoder or BM25 hybrid for precision
• Model: pick smallest that meets quality; add function calling if needed
• Orchestrate: lightweight API with audit logs; rate-limit and retries
• Ops: CI for prompts, dataset of Q/A pairs, red-team tests, dashboards (internal)

Swap components to fit your stack; the pattern matters more than the vendor.

What to measure (so leadership cares)

• Accuracy with citations (automated eval + spot-checks)
• Time saved per draft/ticket; deflection rate; escalation rate
• Privacy incidents (should be 0); policy exceptions
• Adoption: weekly active users, searches, repeated sessions

Anti-patterns to skip

• “Index everything.” Start with trusted content only.
• No owner for the knowledge base. Stale inputs = stale answers.
• Treating GenAI as autonomous. Keep human checkpoints where risk exists.
• No logs. You can’t improve what you can’t inspect.

Your next step

Start with a 10-Day Roadmap Sprint: pick one workflow, connect the right sources, and define the guardrails and metrics. Then ship a permission-aware pilot—fast.

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