FAQ and Macro Snippet Builder for Support Copilots
Distill support knowledge into canonical FAQs, safe macro snippets, and escalation triggers ready for RAG and agent handoffs.
Prompt Overview
Featured AI Partner
Tips For You
- Keep one intent per macro to avoid logic leaks. - Routinely expire macros after 90 days unless re-approved by CX Ops. - Use consistent tags to link macros to product areas for analytics. - Export JSONL chunks for easy ingestion into your RAG index.
From Operations TeamNexusAi TechnologyProblem It Solves
Support teams have duplicative, outdated answers and risky macros. This creates a concise, cited, safe FAQ and macro library.
Intent Clustering
Groups questions to reduce duplication and drift.
Policy-Cited Answers
Every response cites its authoritative source.
Safe Macros
Templates include refusals and escalation triggers.
RAG-Ready Chunks
Exports JSONL with retrieval metadata.
AI Prompt Instructions
Act as: A Customer Support Knowledge Engineer focused on safe, concise, and accurate answers.
Why this task matters: Inconsistent macros and outdated FAQs increase handle time and risk. A canonical, cited set boosts FCR and reduces rework.
Important boundaries:
- Never provide instructions that could change account state unless policy allows.
- Always cite source doc and version for each answer.
- Redact PII and mark with [REDACTED] when examples include sensitive data.
User inputs:
- 50–200 recent tickets or themes, current macros, product FAQ, policy docs.
- Target tone (friendly, professional, concise).
- Escalation criteria.
Objectives:
1) Cluster intents and extract top questions.
2) Draft concise, policy-aligned answers with citations.
3) Build safe macro snippets and escalation triggers.
4) Provide RAG-ready chunks and metadata.
Analysis workflow:
1) Normalize sources and mark authoritative docs.
2) Identify intents, variants, and edge cases.
3) Write answer templates with variables and refusal conditions.
4) Package macros with tags, owners, and review dates.
Required output format:
- Section A: Intent Clusters (table)
- Section B: Canonical Q&A with Citations
- Section C: Macro Snippets (templated)
- Section D: RAG Chunks + Metadata (JSONL)
- Section E: Escalation Rules
Quality controls:
- Max 120 words per answer; include 1–2 citations.
- Include a negative example for each high-risk intent.
Verification checklist:
- Are macros traceable to a policy?
- Do escalation rules cover out-of-scope and missing-data cases?
Final instruction: Output all sections and end with a list of stale or conflicting sources that need owner review.
Expected Outcome
Section C Macro (excerpt) Title: Billing—Refund Window Check Body: "Before proposing a refund, confirm purchase_date ≤ 30 days per Policy 4.2 (Handbook v3 §4.2). If outside window, offer credit per §4.3." Tags: billing, refund, policy
Implementation Journey
Synthesize Q&A in ChatGPT
Paste 50–200 anonymized tickets plus your current macros and policy links into ChatGPT. Run the prompt to generate Intent Clusters and Canonical Q&A with citations and macro drafts.
30-45 minValidate and tag in Notion
Import answers and macros into a Notion database. Add tags for product area, risk level, and review date. Have CX Ops validate citations and tone, then approve items for release.
25-35 minIndex for RAG via n8n
Use n8n to convert Notion entries to JSONL and push to your vector DB. Attach metadata like intent_id and policy_version for precise retrieval in your copilot.
20-30 min