Data privacy & security
- Minimize data exposure: only send necessary text to the LLM (redact secrets, PII, IP addresses, token limits). Use deterministic redaction and a secrets scanner (regex + secrets manager).
- Network & model controls: host models in a VPC or use enterprise APIs with private endpoints and contract clauses (no training on customer data). Enforce TLS, mTLS, and egress/ingress rules.
- Access controls & auditing: RBAC for who can query the assistant, per-call logging, immutable audit trail, and retention policies. Encrypt data at rest and in transit; apply KMS for keys.
- Fail-safe for sensitive docs: require approval before any external model call; prefer on-prem/air-gapped inference for classified content.
Prompt design & grounding to reduce hallucination
- Retrieval-Augmented Generation (RAG): retrieve relevant doc passages and runbooks via dense + lexical search, include only top-k snippets with provenance metadata in the prompt.
- Instruction scaffolding: explicit system prompt that forbids fabrication, requires citations, and formats outputs (summary + confidence + source links).
- Chunking & iterative summarization: summarize sections then synthesize to maintain fidelity.
- Verification chain: add a verification step where the model highlights claims and cites exact excerpt spans and line numbers.
Evaluation metrics for correctness
- Precision of facts: % of model claims verifiable against source (target >95% in high-stakes).
- Citation recall/accuracy: fraction of answers with correct provenance links.
- Runbook relevance & precision: human-labeled relevance (NDCG, precision@k).
- Safety/regression: rate of hallucinated or unsafe suggestions per 1000 queries.
- Latency & throughput for operational SLA.
Human-in-the-loop validation
- Two-tier approval: 1) automated checks (citation presence, redaction, confidence thresholds), 2) mandatory human reviewer for high-risk actions or low-confidence outputs.
- Feedback loop: label model outputs (correct/incorrect/revise) stored for continuous fine-tuning or RAG index weighting. Use active learning to prioritize samples that reduce hallucination.
- UI design: present summaries with provenance side-by-side, show highlighted source snippets, allow quick “accept/edit/reject” and capture rationale.
- Incident workflow integration: if assistant suggests runbook changes, require an on-call engineer sign-off; log decisions to the incident management system.
- Monitoring & alerting: real-time dashboards for hallucination rate, reviewer load, and time-to-approval; roll back model versions if metrics degrade.
Trade-offs & rollout plan
- Start with read-only assistant (summaries + suggested runbooks with citations), measurable human validation, then gradually enable suggested edits after achieving reliability targets. Prioritize critical systems for stricter controls and on-prem models.
