Here’s the truth about AI inside HighLevel: it’s powerful, but it’s also a little unpredictable.
One day your voice bot is booking calls like a champ. The next day? It’s stuck in a loop asking the same question three times like your uncle after two beers. 🍺
That’s where debugging comes in. If you’re not actively testing, tweaking, and documenting your prompts, you’ll end up with bots that confuse more than they convert. Let’s talk about how to actually fix that.
Why AI Goes Sideways
AI bots don’t fail because they’re “broken.” They fail because:
- 📝 Prompts are messy → Too long, unclear, or not prioritized.
- 🔀 Flows aren’t tested → You don’t know what happens when a prospect says something unexpected.
- 📉 Over-refinement → Believe it or not, tinkering endlessly can make the bot worse, not better.
So how do we keep them on track?
My Favorite Debugging Trick
Here’s a hack I’ve used that saves hours: use ChatGPT as your debugging partner.
👉 Run your AI bot on a test call.
👉 Use dictation or transcription to capture the conversation.
👉 Feed it into ChatGPT and ask: “Where did this bot deviate from the expected flow?”
It’s like having a coach sit in on the call, highlight the misses, and point out where your prompts need tightening.
Document Everything (Seriously)
AI systems live or die by your documentation. If you’re not tracking what works, you’re basically rolling dice every time you make a change.
Here’s what I recommend:
- 🗂️ Keep a Google Doc with every version of your prompts.
- 🏷️ Label what worked, what didn’t, and why.
- 🔄 Roll back quickly when a new “fix” actually makes things worse.
Pro Tip: Don’t delete your old prompts. Archive them. Sometimes an earlier version outperforms your latest “improvement.”
When AI Isn’t the Answer
This might sound weird coming from me, but sometimes AI isn’t the right solution.
If you’re bending over backward trying to force AI into a use case that should be a simple automation or workflow, you’re going to waste time and frustrate clients.
👉 Always ask: “Is AI the best tool for this job—or am I overcomplicating it?”
Sometimes a simple trigger beats a fancy prompt.
The Big Takeaway
Debugging AI isn’t about chasing perfection—it’s about keeping your bots aligned with your business goals.
✅ Use ChatGPT as your debugging buddy.
✅ Document everything so you know what works.
✅ Stop “over-optimizing” your prompts into oblivion.
✅ And don’t be afraid to admit: sometimes, a workflow beats AI.
Do that, and your bots will go from awkward robots to reliable revenue machines.
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Frequently Asked Questions
Why AI Goes Sideways?
AI bots don’t fail because they’re “broken.” They fail because: 📝 Prompts are messy → Too long, unclear, or not prioritized. 🔀 Flows aren’t tested → You don’t know what happens when a prospect says something unexpected. 📉 Over-refinement → Believe it or not, tinkering endlessly can make the bot worse, not better. So how do we keep them on track?
What should I know about my Favorite Debugging Trick?
Here’s a hack I’ve used that saves hours: use ChatGPT as your debugging partner. 👉 Run your AI bot on a test call.👉 Use dictation or transcription to capture the conversation.👉 Feed it into ChatGPT and ask: “Where did this bot deviate from the expected flow?” It’s like having a coach sit in on the call, highlight the misses, and point out where your prompts need tightening.
What should I know about document Everything (Seriously)?
AI systems live or die by your documentation. If you’re not tracking what works, you’re basically rolling dice every time you make a change. Here’s what I recommend: 🗂️ Keep a Google Doc with every version of your prompts. 🏷️ Label what worked, what didn’t, and why. 🔄 Roll back quickly when a new “fix” actually makes things worse. Pro Tip: Don’t delete your old prompts. Archive them. Sometimes an earlier version outperforms your latest “improvement.”
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