Cognigy Prompts

Draft a real-time guidance script for a Cognigy Agent Copilot deployment at [COMPANY]. The script must cover Identity Assist at handover, Knowledge Assist for surfacing [PRODUCT] answers, Action Assist steps for [PROCESS_NAME], Language Assist triggers, and Wrap-up Assist one-click summaries. Keep the tone friendly and professional for [AGENT_TEAM] agents handling [TICKET_VOLUME] daily conversations.

Create a tuning checklist for a Knowledge AI RAG index in Cognigy.AI that powers support for [COMPANY] in [INDUSTRY]. Include source quality rules, document refresh cadence, chunking guidance, retrieval thresholds, guardrails against hallucination, fallback phrasing, and a weekly review routine for the [KNOWLEDGE_OWNER] role. Output as a numbered checklist with owners for each item.

Write deflection content for a Cognigy.AI self-service agent at [COMPANY]. Cover the top [TOP_N] repeat reasons from [CHANNEL_LIST]: [REASON_1], [REASON_2], [REASON_3]. For each reason give a short bot answer, an xApps embed suggestion (photo upload, location, form, or payment), escalation wording, and a deflection success metric. Keep answers under 60 words and in plain language.

Design intelligent routing rules for [COMPANY] in Cognigy Live Agent. Define intent-to-queue mapping for [INTENT_LIST], skill-based routing by [AGENT_SKILLS], priority rules for [VIP_SEGMENT], language-based routing for [LANGUAGES], and queue overflow behavior after [WAIT_THRESHOLD]. Output a rule table plus test scenarios showing a conversation routed correctly to each queue.

Build a QA scorecard for AI agent conversations in Cognigy Insights at [COMPANY]. Score 1 to 5 on: intent accuracy, RAG answer correctness, empathy and tone, escalation timing, and wrap-up completeness. Add pass/fail thresholds, calibration notes for reviewers, and three sample failure comments to flag. Keep the card printable on one page for the [QA_TEAM] team.

Create a CSAT recovery playbook for [COMPANY] using Cognigy.AI. Cover detection of low scores from [SURVEY_CHANNEL], automated outreach within [TIME_WINDOW], a three-message recovery sequence for [ISSUE_TYPE], Agent Copilot warm handover briefing format, compensation approval levels, and a follow-up survey. Include ticket reference [TICKET_ID] and escalation to [MANAGER_NAME].

Write a set of canned responses and macros for Cognigy Live Agent at [COMPANY]. Create templates for [SCENARIO_1], [SCENARIO_2], and [SCENARIO_3], each with personalization placeholders like [CUSTOMER_NAME] and [ORDER_ID], a knowledge-link variant, and an escalation variant. Keep each message under 80 words and compliant with [COMPLIANCE_POLICY].

Design an analytics report spec for Cognigy Insights at [COMPANY]. Track containment rate, first contact resolution, average handle time, escalation rate, Copilot assist acceptance rate, and CSAT trend over [PERIOD]. Specify filters by channel ([CHANNELS]), language ([LANGUAGES]), and intent ([INTENT_GROUPS]), plus weekly export format and owner [ANALYST_NAME]. Define red and green thresholds per KPI.

Create a chatbot training intent pack for a Cognigy.AI NLU model at [COMPANY]. For intent [INTENT_NAME] write [N] training utterances, [N] negative examples, entity definitions for [ENTITY_LIST], synonym lists, and edge cases where [INTENT_NAME] overlaps with [OTHER_INTENT]. Output JSON-ready format with confidence targets above [CONFIDENCE_SCORE].

Write a 30-60-90 day onboarding playbook for launching a Cognigy.AI voice and chat agent at [COMPANY] in [INDUSTRY]. Day 30: use case scoping, knowledge source setup, and pilot scope for [PILOT_SCOPE]. Day 60: Voice Gateway integration, intent training, xApps embeds, and Live Agent handover setup. Day 90: Insights dashboards, QA cadence, and scale plan to [SCALE_TARGET] conversations.