Dixa Prompts

Write a Dixa AI Co-Pilot configuration prompt for [COMPANY] agents handling [TICKET_TYPE] conversations. Define tone rules, which customer data to pull from the Agent Hub profile, when to use Smart Conversation Replies versus a free-text reply, and when the agent must edit or escalate instead of sending. Add placeholders for order number, plan tier, and SLA window so drafts stay accurate across all [TEAM_NAME] queues.

Audit the Dixa Knowledge Base for [COMPANY] covering [TOPIC_AREA]. Identify the 10 most-answered articles from Smart Conversation Summaries data, flag outdated steps, and rewrite weak articles with clear headings, screenshots, and a TL;DR. Link each article to its matching canned response macro and set a 90-day review cycle owned by [OWNER_ROLE].

Draft self-service deflection content for [COMPANY] that Mim can answer inside Dixa Conversation Engine flows. Cover the top 8 repeat questions from [DATA_SOURCE], write a 3-step flow for each with a clear fallback to a human agent, and add quick links to the Knowledge Base. Define success as a containment rate above [TARGET_PERCENT] and a handoff CSAT above [CSAT_TARGET].

Design intelligent routing rules in Dixa for [COMPANY] across [CHANNEL_LIST]. Map skill-based routing so billing, technical, and VIP customers reach the right [TEAM_NAME] agents, set conversation priority and queue rules, define overflow to callback requests, and document each rule inside the Conversation Engine. Include three test scenarios and a fallback path when no agent is available.

Build a QA scorecard for [COMPANY] in Dixa Discover using real conversation data. Score greeting, accuracy, tone, resolution, and compliance, weight each criterion out of 100, and write concrete rubric examples for pass, borderline, and fail. Add a monthly calibration routine for reviewers and tie low scores to coaching plans for [TEAM_NAME] agents with follow-up checks.

Create a CSAT recovery workflow for [COMPANY] in Dixa. Trigger when a conversation rated below [THRESHOLD] closes: auto-assign the ticket to a senior [TEAM_NAME] agent, draft a Smart Conversation Reply apology using the customer profile data, and schedule a follow-up in [TIMEFRAME]. Log root cause tags in Dixa Discover so weekly reports show which issues repeat most.

Create a canned response macro library in Dixa for [COMPANY] covering [TOPIC_LIST]. Each macro needs a trigger keyword, placeholders for customer name and ticket data, a clear escalation line, and a link to the matching Knowledge Base article. Keep every reply under 80 words, aligned with [COMPANY] tone rules, and reviewed by [OWNER_ROLE] before publishing to agents.

Design a real-time analytics dashboard spec for [COMPANY] in Dixa Discover covering first response time, resolution rate, CSAT, channel mix, and agent performance. Define date ranges, filters by [TEAM_NAME], alert thresholds for each metric, and a weekly summary format for [STAKEHOLDER_ROLE]. Add one insight callout per metric explaining what action to take when it moves.

Create chatbot training intents for Mim in Dixa for [COMPANY]. List the top 10 intents from [DATA_SOURCE], write five natural example utterances per intent, define slots like [ORDER_ID] and [ACCOUNT_EMAIL], and set fallback plus handoff rules into Agent Hub. Note how to measure containment rate per intent in Dixa Discover after launch and when to retire low-value intents.

Write a 30-60-90 day agent onboarding playbook for [COMPANY] on Dixa. Week one: omnichannel inbox, Agent Hub, and Smart Conversation Summaries basics. Day 30: canned responses and macros with supervised shifts. Day 60: QA scorecard practice and intelligent routing rules. Day 90: solo shifts with queue access. Add checklists, a mentor pairing plan, and sign-off criteria for [TEAM_NAME] leads.