Build an AI Customer Support Chatbot

Step-by-step tutorial to build and deploy an AI customer support chatbot with PromptOwl in 15 minutes using RAG and knowledge base.

Build a production-ready AI customer support chatbot that answers questions from your documentation, handles common inquiries, and escalates when needed. No coding required.

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What You'll Build

A customer support chatbot that:

• Answers questions from your support documentation

• Provides accurate responses with source citations

• Handles FAQs automatically

• Knows when to escalate to human support

• Can be embedded on your website or accessed via API

Time required: 15 minutes Difficulty: Beginner Prerequisites: PromptOwl account and support documentation

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Step 1: Prepare Your Knowledge Base (3 minutes)

Your chatbot needs documentation to answer from. Gather your:

• FAQ documents

• Product documentation

• Support articles

• Policy documents (returns, shipping, etc.)

Supported Formats

Format	Best For
PDF	Product manuals, policies
DOCX	Support articles
TXT	FAQ lists
CSV	Structured data (pricing, specs)

Upload to Data Room

1. Click Data Room in the left sidebar

2. Click Create → Folder

3. Name it "Customer Support Docs"

4. Open the folder and click Create → Artifact

5. Select File and upload your documents

6. Repeat for all your support documentation

Tip: Organize documents by topic (e.g., "Shipping", "Returns", "Product Info") for better retrieval.

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Step 2: Create Your Support Agent (5 minutes)

Start a New Agent

1. Click + New on the Dashboard

2. Enter a name: "Customer Support Bot"

3. Add a description: "Answers customer questions from support documentation"

Write the System Prompt

This is where you define your chatbot's behavior. Copy and customize this template:

You are a friendly and helpful customer support assistant for [YOUR COMPANY NAME].

Your role:
- Answer customer questions accurately using the provided documentation
- Be concise but thorough
- Use a warm, professional tone
- Always cite your sources when referencing specific policies or information

Guidelines:
- If you find the answer in the documentation, provide it with confidence
- If you're unsure or the question is outside your knowledge, say: "I don't have that specific information. Let me connect you with our support team at [[e-mail]]"
- For urgent issues (account access, billing disputes, safety concerns), always recommend contacting human support
- Never make up information - only use what's in the provided documents

Company information:
- Support email: [[e-mail]]
- Support hours: [Monday-Friday, 9am-5pm EST]
- Website: [yourwebsite.com]

Connect Your Knowledge Base

1. In the prompt editor, find the Dataset field in block settings

2. Click Connect Data

3. Select the "Customer Support Docs" folder you created

4. This enables RAG - your bot will search these documents to answer questions

Configure the Model

1. Select your LLM provider (OpenAI, Anthropic, etc.)

2. Choose a model:

   • GPT-4o or Claude 3.5 Sonnet - Best quality

   • GPT-4o-mini or Claude 3 Haiku - Faster, cheaper for high volume

3. Set temperature to 0.3 (lower = more consistent answers)

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Step 3: Test Your Chatbot (3 minutes)

Before deploying, test with real customer questions.

Test Questions to Try

Ask questions that customers actually ask:

"What is your return policy?"
"How long does shipping take?"
"How do I reset my password?"
"Can I change my order after placing it?"
"What payment methods do you accept?"

Check for Quality

For each response, verify:

• Answer is accurate (matches your documentation)

• Citations appear (showing source documents)

• Tone is appropriate

• Escalation works for unknown questions

Iterate on the Prompt

If responses aren't right:

• Too verbose? Add "Be concise, limit responses to 2-3 paragraphs"

• Wrong tone? Adjust the personality description

• Missing info? Check if documents are uploaded and synced

• Hallucinating? Add "Only answer based on the provided documentation"

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Step 4: Deploy Your Chatbot (4 minutes)

Option A: Embed on Your Website

1. Go to the Publish tab

2. Toggle status to Live

3. Find Chatbot Embed Generator

4. Copy the iframe code:

<iframe
  src="https://promptowl.ai/chatPopup/YOUR_SESSION_ID/YOUR_PROMPT_ID"
  width="400"
  height="600"
  style="border: none; position: fixed; bottom: 20px; right: 20px;">
</iframe>

1. Paste into your website's HTML

2. Customize size and position as needed

Option B: Use the API

For custom integrations:

1. Go to the Publish tab

2. Click Generate API Key

3. Save your key (it won't be shown again)

4. Make API calls:

curl -X POST https://promptowl.ai/api/prompt/YOUR_PROMPT_ID \
  -H "Content-Type: application/json" \
  -H "X-API-Key: po_your-api-key" \
  -d '{
    "sessionId": "customer-123",
    "message": "What is your return policy?"
  }'

Customize Appearance

In the Publish tab, customize:

• Header background color

• Text colors

• User message bubble color

• Hide/show branding

Match your brand colors for a seamless experience.

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Step 5: Monitor and Improve (Ongoing)

Your chatbot is live! Now monitor its performance.

Track Quality with Annotations

1. Go to Monitor tab

2. Review conversations

3. Look for:

   • Questions with negative feedback

   • Unanswered questions

   • Incorrect responses

Add Missing Knowledge

When you find gaps:

1. Create new documentation covering the topic

2. Upload to your Data Room

3. Documents sync automatically

Use Evaluation Sets

For systematic quality tracking:

1. Go to Evaluate tab

2. Create an evaluation set with common questions and expected answers

3. Run evaluations after prompt changes

4. Track improvement over time

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Advanced: Make It Smarter

Add Conversation Memory

Enable memory so the bot remembers context:

1. In prompt settings, enable Memory

2. Use {memory} variable in your prompt

3. Bot now remembers previous messages in the conversation

Handle Multiple Topics

For complex support needs, consider a Supervisor Agent:

• Billing Agent - handles payment questions

• Technical Agent - handles product issues

• Shipping Agent - handles delivery questions

• Supervisor routes to the right specialist

See Understanding Agents for details.

Connect to Your Systems

Use the API to integrate with:

• Your CRM (log conversations)

• Ticketing system (auto-create tickets)

• Analytics (track common questions)

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Troubleshooting

Bot says "I don't know" for documented topics

1. Check document is uploaded and synced (green status)

2. Verify document content is text (not scanned images)

3. Try rephrasing the question

4. Check chunk preview to see how content is split

Responses are too slow

1. Switch to a faster model (GPT-4o-mini, Claude Haiku)

2. Reduce max tokens in settings

3. Check your internet connection

Bot hallucinates information

1. Lower temperature to 0.1-0.3

2. Add explicit instruction: "Only answer from provided documentation"

3. Add: "If unsure, say you don't know"

Citations not appearing

1. Verify RAG is connected (Dataset field has content)

2. Check artifacts have "Title for Citation" filled in

3. Ensure enterprise settings allow citations

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What's Next?

You've built a working customer support chatbot! To take it further:

• Set up evaluation sets for quality tracking

• Add tools for calculations or web search

• Share with your team for collaboration

• Deploy via API for custom integrations

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Summary

Step	Time	What You Did
1. Knowledge Base	3 min	Uploaded support documentation
2. Create Agent	5 min	Wrote prompt, connected RAG
3. Test	3 min	Verified quality and accuracy
4. Deploy	4 min	Embedded or API published
Total	15 min	Production customer support bot

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Ready to build? Sign up for PromptOwl and create your first agent today.