Great assistants combine retrieval, tools, and policy. Use RAG to ground responses in your docs; chunk semantically, store embeddings with metadata, and rerank results to avoid hallucinations. Add tool use for real actions—tickets, knowledge search, CRM updates—with strict schemas and timeouts. Keep latency budgets (p95 under 3s) and degrade gracefully when upstreams fail.
Ship with an evaluation harness. Track exact-match, factuality, and safety on golden datasets; auto-run evals in CI for every prompt or model change.
Ship with an evaluation harness. Track exact-match, factuality, and safety on golden datasets; auto-run evals in CI for every prompt or model change. Add telemetry—spans, tokens, sources used—so you can debug why an answer went wrong. Implement guardrails: PII redaction, content filters, policy prompts, and allowlists for tools.
Adoption hinges on UX. Give users visibility (what data was used), easy escalation to humans, and action confirmations. Start narrow—support triage, sales QA, IT self-service—show lift, then expand. With logs, retries/backoff, and clear audit trails, your assistant becomes a dependable teammate, not a demo.