How do I set up a reply suggestion tool for knowledge-grounded drafting with human approval required?

To set up a reply suggestion tool for knowledge-grounded drafting with human approval required, design it as a drafting assistant, not an auto-sender. A good setup looks like this:

1) Define the workflow

Use a simple approval loop:

1. User asks for a reply suggestion
2. System retrieves relevant knowledge from approved sources
3. Model drafts a response grounded in that knowledge
4. Human reviews, edits, and approves
5. Only then can the reply be sent

This keeps the model from acting autonomously.

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2) Connect trusted knowledge sources

Use sources such as:

• internal docs / help center
• policy pages
• product manuals
• CRM notes
• ticket history
• approved FAQs

Best practice:

• limit retrieval to curated, up-to-date sources
• include citations or source snippets in the draft
• tag sources by freshness, ownership, and relevance

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3) Use retrieval-augmented generation (RAG)

Typical architecture:

• Input: customer message / draft request
• Retriever: searches knowledge base
• Context builder: selects top relevant passages
• LLM: generates a suggested reply using only that context
• Guardrail: checks that answer is grounded and doesn’t invent facts
• Approval UI: human accepts/edits/rejects

Key prompt instruction:

• “Answer only using provided knowledge. If information is missing, say so and suggest escalation.”

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4) Require explicit human approval

Make approval a hard gate in the product workflow:

• Draft state: suggested
• Human actions: edit, approve, reject
• Send state: approved_and_sent

Important:

• the system should not have permissions to send automatically
• log who approved it and when
• preserve the original draft and final version for audit

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5) Add confidence and safety checks

Before showing the draft, run checks for:

• unsupported claims
• policy violations
• missing citations
• outdated or conflicting sources
• sensitive data exposure

If confidence is low:

• show “needs review”
• suggest a safer, shorter reply
• ask the human to verify details

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6) Keep the assistant constrained

Helpful constraints:

• fixed tone and style guidelines
• allowed/disallowed topics
• escalation rules
• no hallucinated pricing, legal, medical, or policy claims
• “ask a clarifying question” when context is insufficient

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7) Build the UI around review

Your approval screen should show:

• customer message
• retrieved source excerpts
• drafted reply
• confidence/quality flags
• edit box
• approve/reject buttons

This makes review fast and grounded.

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8) Log and improve

Store:

• retrieved sources
• model output
• human edits
• approval decisions
• final sent message
• feedback on correctness

Use this to:

• improve retrieval quality
• identify weak sources
• refine prompts
• measure time saved and error rates

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9) Example prompt pattern

A useful draft prompt:

You are a reply drafting assistant. Use only the provided knowledge snippets to draft a concise, helpful response. If the answer is not fully supported by the snippets, explicitly say what is unknown and recommend escalation or a follow-up question. Do not send the reply yourself. Provide a draft for human approval.

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10) Minimal implementation checklist

• approved knowledge base
• retrieval pipeline
• grounded response prompt
• citations/snippets in UI
• human approval gate
• audit logs
• safety checks
• escalation path

If you want, I can also give you:

1. a system architecture diagram,
2. a sample prompt + JSON schema, or
3. a product requirements spec for this tool.