How to Test Your GHL Chatbot Before Going Live (Step-by-Step)

Most agencies launch bots with what amounts to a gut check. Open Live Chat, ask "What are your hours?", watch the bot answer correctly, and call it tested. Then a real customer asks about cancellation policies and the bot invents one on the spot.

Proper pre-launch testing isn't optional — it's the difference between a bot that builds your client's trust and one that silently destroys it. Here's the exact process we use to test GHL chatbots across 8 channels with 100+ scenarios before a single real customer touches them.

Step 1: Build Your Scenario Library

Before you touch the bot, you need to define what "working correctly" looks like. That means writing test scenarios — scripted customer conversations that cover every capability the bot should have and several it shouldn't.

Start with five categories:

• Knowledge Base queries — Questions the bot should be able to answer from its KB. Include exact matches ("What are your hours?"), paraphrased versions ("When are you guys open?"), and edge cases ("Are you open on Christmas?").

• Action triggers — Every action the bot can take. Appointment booking, cancellation, rescheduling, contact field updates, human handover. Write scenarios that trigger each one, plus scenarios that are close but shouldn't trigger them ("I'm thinking about canceling" vs. "Cancel my appointment").

• Objection handling — Price objections, competitor comparisons, skepticism. "That's too expensive." "Why should I pick you over [competitor]?" "I read bad reviews about you."

• Off-topic and boundary testing — Questions the bot should deflect. Medical advice, legal questions, personal opinions, inappropriate requests. The bot needs to handle these gracefully without making things up.

• Multi-turn conversations — Real customers don't ask one question and leave. Write scenarios with 4-6 turns where the customer changes topics, circles back, or gets increasingly specific.

Aim for 15-25 scenarios minimum per bot. It sounds like a lot, but real customers will hit you with far more variety than that.

Step 2: Set Up Isolated Test Contacts

Never test with real customer contacts. Create dedicated test contacts for each scenario run. This matters because GHL's conversation AI retains full context per contact — if you reuse a contact across multiple test scenarios, the bot's responses to scenario 5 are influenced by everything it "remembers" from scenarios 1-4.

The right way:

1. Create a new contact in GHL for each scenario
2. Do NOT add a phone number — this prevents the contact from being routed through SMS/Twilio, which costs money and burns number reputation
3. Use a naming convention that's easy to identify and clean up: "TEST - Scenario 12 - Booking Flow"
4. After the scenario completes, delete the contact immediately

This contact isolation is non-negotiable. Skip it and your test results are unreliable.

Step 3: Test the Opening Message

How the conversation starts matters. There are two approaches depending on your bot's configuration.

Direct mode: The bot sends an opening message when a new conversation is created. Verify this message is accurate, on-brand, and sets the right expectations. Does it mention the correct business name? Does it reference the right offer? Is the tone appropriate?

Workflow mode: The conversation is triggered by a GHL workflow (e.g., a form submission adds the contact to a workflow that initiates the bot). Test the full trigger path — submit the form, verify the workflow fires, check that the bot's opening message arrives within a reasonable time (under 30 seconds).

Document the opening message in your test log. If it's wrong, everything that follows is built on a broken foundation.

Step 4: Run Scenarios Across Every Active Channel

This is where most agencies cut corners. They test on Live Chat and assume the bot works everywhere. It doesn't.

GHL supports multiple conversation channels: Live Chat, SMS, Facebook Messenger, Instagram, WhatsApp, Google Business Messages, Email, and TikTok. Your bot might be configured on any combination of these.

For each active channel:

1. Verify the conversation agent is actually enabled on that channel (GHL Settings > Conversation AI > Agent > Channels)
2. Send the opening customer message through that channel
3. Watch for channel-specific issues:
   • SMS: Response too long? Links formatted correctly? Character encoding issues?
   • Instagram/Facebook: Bot responding at all? Media handling correct?
   • Email: Subject line appropriate? Response doesn't look like spam?
   • WhatsApp: Template message requirements met?

At minimum, test every scenario on Live Chat (your baseline) and at least one other channel the client uses heavily.

Step 5: Test Knowledge Base Accuracy

The knowledge base is the foundation of every bot response. Testing it means verifying both what the bot knows and what it thinks it knows.

Positive tests (should answer correctly):

• Ask about every service listed in the KB
• Ask about pricing for each service
• Ask about business hours, location, team members
• Ask about policies (cancellation, refund, late arrival)

Negative tests (should NOT fabricate answers):

• Ask about services the business doesn't offer
• Ask about pricing for services not in the KB
• Ask about competitors by name
• Ask detailed technical questions beyond the KB scope

Staleness tests:

• Ask about any promotions mentioned in the KB — are they still current?
• Ask about team members — has anyone left?
• Ask about hours — have they changed?

For every response, compare what the bot says against the actual KB content. If the bot adds information that isn't in the KB, that's a hallucination and needs to be fixed.

Step 6: Test Every Action

GHL conversation agents can trigger several action types: appointment booking, contact field updates, advanced follow-up, stop bot, and human handover. Test each one.

For each action:

1. Send a message that should trigger the action
2. Verify the action actually fires (check the contact record, calendar, or conversation status)
3. Send a message that's similar but should NOT trigger the action
4. Verify the action does NOT fire

Pay special attention to the Stop Bot action. This one causes the most problems because it's often configured with broad keyword triggers. If "cancel" triggers Stop Bot, your cancellation flow is broken.

Step 7: Test Edge Cases and Failure Modes

These are the scenarios that never come up in manual testing but happen constantly with real customers.

• Typos and misspellings: "I wanna book a apointment for botx"
• Multiple intents in one message: "What are your hours and how much is a facial?"
• Rapid-fire messages: Send 3 messages before the bot responds — does it handle all of them?
• Contradictions: "I want to book Tuesday. Actually, make that Wednesday."
• Language switching: If the business serves bilingual customers, try mixing languages
• Empty or single-word messages: "Hi" / "ok" / "?" / "..."
• Frustration escalation: Start polite, get progressively more frustrated — does the bot recognize the shift?

Step 8: Grade Everything

Don't just check "did it respond?" Use a structured rubric for every response.

Four things to evaluate:

1. Was the information accurate? Did it match the KB? Did it make anything up?
2. Did the bot take the right action? Did it book when it should? Escalate when it should?
3. Was the tone appropriate? Does it match the brand voice? Too formal? Too casual?
4. Was the response safe? No hallucinated medical/legal advice? No leaked data? No inappropriate content?

Score each scenario as Pass, Warn (partially correct), or Fail. Keep a log with the exact bot response for every test.

Step 9: Document and Share Results

Your test results are a deliverable. Write them up in a format you can share with the client:

• Total scenarios tested
• Pass/Warn/Fail breakdown
• Top failures with specific examples
• Recommended fixes
• Channel-by-channel performance

This report does double duty: it guides your fixes and demonstrates your thoroughness to the client. Agencies that share pre-launch test reports close deals faster because clients see the rigor behind the work.

Step 10: Fix and Retest

Fix every Fail. Review every Warn. Then run the entire suite again. Testing isn't a one-pass process — fixes to one scenario can break another.

The goal is a clean pass across all scenarios on all channels before the bot goes live. That's the standard. Anything less and you're gambling with your client's customers.

This is exactly the workflow BadBots.ai automates — scenario creation, multi-channel execution, structured grading, and shareable reports. But the process works whether you run it by hand or with tooling. The important thing is that you run it at all.