I Tested 3 AI Prompts for Customer Support — Here's What I Learned

Harshika Gahlot — Fri, 23 Oct 2026 05:56:23 +0000

I'm currently learning about AI and exploring how it can be applied to solve real-world business problems. My long-term goal is to become an AI Solutions Consultant, and I'm starting by understanding the fundamentals and experimenting with AI tools.

Today, I worked on a practical exercise using Claude to analyze a customer complaint, summarize the issue, and draft a customer support email.

The interesting part? I tested three different prompts using the exact same customer inquiry.

The Experiment

Imagine a customer who ordered wireless headphones 10 days ago. The package is still marked "in transit," the expected delivery date has passed by five days, and the customer has already contacted support twice without receiving a useful update.

There's another complication: the customer needs the headphones for an online interview tomorrow.

I asked Claude to classify the inquiry, summarize the customer's problem, and draft a response.

Attempt 1: A Basic Prompt

I simply asked Claude to classify the inquiry, summarize the problem, and draft a response.

The result was detailed, empathetic, and proactive. It suggested contacting the carrier, offering a replacement or refund, and following up with the customer.

However, it also assumed that certain solutions were available without knowing the company's actual policies.

Attempt 2: A More Specific Prompt

Next, I added clear instructions about classification, sentiment, urgency, summary length, and the expected email format.

The output became more structured, but it still suggested replacement and refund options that required verification.

This made me realize that specifying the output format doesn't automatically ensure factual reliability.

Attempt 3: A Structured Prompt with Clear Boundaries

For the third attempt, I added requirements to distinguish confirmed facts from missing information, avoid unsupported promises, and identify what needed verification.

This response was less conversational than the first one, but it handled uncertainty more responsibly.

It didn't promise a refund, replacement, or delivery by the interview deadline. It also identified information that a support agent needed to verify.

There was still room for improvement: the email could have sounded warmer, and it should not claim that an investigation has started unless that action has actually been taken.

What I Learned

A longer prompt doesn't automatically produce a better result.

The instructions need to be clear and relevant, not simply lengthy.

Helpful-sounding AI responses can contain unsupported assumptions.

An answer may sound confident and empathetic while promising something the company cannot guarantee.

AI must recognize the limits of the information available to it.

When important details are missing, identifying what needs verification is often better than inventing an answer.

Accuracy and empathy must work together.

A reliable response should be factually responsible without making the customer feel dismissed.

Evaluating AI output is as important as generating it.

Instead of accepting the first response, comparing multiple outputs helped me identify differences in structure, accuracy, and communication quality.

My Biggest Takeaway

This exercise helped me understand that using AI effectively is not just about asking it to generate content. It's about evaluating whether the output is appropriate for the real-world situation.

As I continue learning, I want to explore how AI can help businesses improve workflows, solve operational problems, and make better use of their resources.

I'm documenting these experiments as I progress from learning AI fundamentals toward designing practical AI solutions.

Tools used: Claude
Experiment: Customer inquiry classification, summarization, and response drafting

This is the beginning of my learning journey, and I'm looking forward to testing more ideas, learning from mistakes, and building practical solutions.

If you've experimented with AI for customer support or business workflows, I'd love to hear what you've learned!

DEV Community: Harshika Gahlot

I Tested 3 AI Prompts for Customer Support — Here's What I Learned

AI #LearningInPublic