Most SMB support inboxes are 80% repeat questions: hours, pricing, where's my order, how do I do X. A RAG (retrieval-augmented generation) agent reads your docs and your past resolved tickets, answers the easy questions in seconds with citations, and only hands the genuinely novel issues to a human — pre-summarized with relevant context from prior tickets. The pattern works whether you build it custom (Pinecone + OpenAI) or buy it (Intercom Fin, Chatbase).
Help center articles, internal SOPs, past resolved tickets (last 12 months), product docs, FAQ. Quality matters more than volume — a clean 200-doc corpus beats a sloppy 2,000-doc one.
Split each doc into ~500-token chunks with metadata (source, last_updated, category). Embed with text-embedding-3-large. Store in Pinecone, Supabase pgvector, or use Chatbase if no-code.
On each query: retrieve top 5 chunks → pass to GPT-4 with strict instruction: 'Answer using ONLY the provided context. If the context doesn't answer the question, say so and offer to escalate. Cite sources by URL.'
Score the answer's confidence (low retrieval similarity, hedging language, missing entities mentioned in question). Below threshold → auto-escalate to human with the question + retrieved context + the agent's draft attempt.
Start with the help-widget on your site or one specific email alias. Don't start in your main support inbox. Watch resolution rate for 2 weeks before expanding.
Every answer ends with 'Was this helpful? 👍/👎'. Negative responses + escalated tickets feed back into the corpus as 'known gaps' for human reviewers to write new docs.
Schedule a re-embedding job that picks up new docs and resolved tickets. Stale corpus is the #1 reason RAG agents degrade.
Use these templates as-is or customize for your business.
You are a support agent for [Company]. Answer the user's question using ONLY the context provided below. If the context does not answer the question or you're less than 90% confident, respond exactly with: 'ESCALATE: <one-sentence reason>'. Cite sources by their URL inline like [source: https://...]. Never invent product features, prices, or policies.
Context:
{{retrieved_chunks}}
Question: {{user_question}}
Answer:Escalate to human if ANY of: (a) top retrieved chunk similarity < 0.75, (b) answer contains 'I think', 'possibly', 'might be', (c) question references a specific account/order/case ID (always human-handled), (d) sentiment classifier scores user message as 'angry' or 'urgent'.
🚨 *Escalated: {{ticket_id}}*
📩 Question: {{user_question}}
🤖 Agent attempted: {{agent_draft}}
📚 Retrieved context: [link to top 5 chunks]
💡 Likely gap: {{detected_gap}}
👤 Assigned to: {{round_robin_agent}}Get a new AI workflow every week. Prompts, tool stacks, and ROI math included.
Retrieval-augmented generation: the agent answers strictly from a curated corpus of your documents and history. Cheaper, more controllable, and fewer hallucinations than open-ended generation.
Learn the agentic glossary →Where this workflow tends to break in production — and what to put in place before you ship it.
Confidently wrong answers (hallucination)
Mitigation: Strict 'context-only' prompt + confidence gate + mandatory escalation on low retrieval similarity.
Stale corpus drift
Mitigation: Weekly re-embed job; tag chunks with last_updated; downrank stale ones.
PII leakage in citations
Mitigation: Pre-scrub PII from past tickets before embedding.
Do not deploy on workflows requiring human judgment, legal advice, medical advice, or financial advice. Do not deploy without a clean knowledge corpus — garbage in produces confidently wrong answers. Do not skip the human-in-loop review during the first month.
A phased approach to get this workflow running and delivering ROI.
Days 1–30
Foundation
Days 31–60
Optimization
Days 61–90
Scale
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Get the full guide with step-by-step setup, workflow templates, and copy-paste assets.