AI-Powered Dialog Message Review — Part 1

Fixing Bad Dialog Messages with Generative AI: A UX-Driven Approach

This is Part 1 of a 2-part series on dialog messages. this first article introduces the big idea and provides a few sample AI prompts to get started. Part 2 includes more hands-on examples, expanded component suggestions, and AI-generated CSV outputs.

Introduction

Life is filled with complex desktop applications, web platforms, and mobile apps yet one of the most overlooked elements they share is the dialog message — a simple yet critical touchpoint in user interaction. As projects grow, dialog messaging is often neglected, left to team members without expertise in technical writing or UX design. The result? Confusing, frustrating, and inconsistent experiences.

But there’s a solution: Generative AI can systematically find, refine, and improve dialog messages, turning frustration into clarity and poor experiences into intuitive interactions.

Why Dialog Messages Need More Attention

Dialog messages aren’t just functional notifications — they’re conversations between the system and the user. When well-crafted, they:
✔️ Guide users toward solutions.
✔️ Reduce frustration and support tickets.
✔️ Build trust in digital experiences.

But when poorly written, they can:
❌ Confuse users with technical jargon.
❌ Lead to frustration, abandonment, or incorrect actions.
❌ Increase the customer support burden.

This isn’t just a designer’s responsibility — developers, product managers, and copywriters all play a role in ensuring dialog messages are clear, helpful, and actionable.

How Generative AI Can Help

AI-powered tools can analyze, refine, and modernize dialog messages by:

• Detecting ambiguity, poor phrasing, and inconsistencies.
• Rewriting messages to be clear, action-oriented, and user-friendly.
• Providing context-aware refinements based on UX best practices.
• Suggesting appropriate UI components for better message delivery.

To systematically improve dialog messages, we can break the AI-assisted refinement process into three key areas:

1. Assessment

AI evaluates the clarity and effectiveness of existing dialog messages.
✔️ Does the message make sense?
✔️ Is it clear what action the user should take?
✔️ Does it use plain language instead of technical jargon?

2. Suggested Changes

AI refines message titles, descriptions, and button labels while maintaining original intent.
✔️ Can we make the message shorter and clearer?
✔️ Does it align with UX writing best practices?
✔️ Can we offer more specific guidance to the user?

3. Component Suggestions

AI helps align dialog messages with appropriate UI components for better usability. Not every message needs to be a dialog box.
✔️ Should this be a dialog, alert, or inline message?
✔️ What severity level should it be? (Success, Warning, Critical, Info)
✔️ How does this fit within the product’s broader UX patterns?

By using AI as a collaborative partner — not a replacement for human judgment — teams can streamline dialog messaging improvements and ensure a better user experience.

Try It Yourself: AI-Powered Dialog Message Review

Want to see how AI can help improve dialog messages? Below are two different approaches for using AI to review and refine dialog messages:

1. Reviewing Plain Text Dialog Messages — For teams that store dialog messages in structured text files like CSV, JSON, or localization files.
2. Extracting & Structuring Dialog Messages from Code — For teams that have hardcoded dialog messages buried in source code and need to extract them for review.

Copy and paste the following AI prompts into ChatGPT (or any other AI tool), then provide a sample dialog message or code snippet to analyze. AI will assess the message, suggest improvements, and structure the output for easy review by designers, technical writers, and product owners.

Reviewing Plain Text Dialog Messages

Task:
You are a UX writing assistant specializing in improving user-facing dialog messages. Analyze the provided message and identify issues related to clarity, tone, actionability, and consistency. Then, suggest an improved version that enhances user understanding and recovery.

Input Format:
Original Title: [Title of the dialog message, if applicable]
Original Description: [Main body of the dialog message]
Original Buttons: [Button text, if applicable]

Output Format:
Assessment:
Does the message clearly explain the issue?
Is the tone professional, friendly, and appropriate?
Does it provide actionable next steps for the user?

Suggested Improvements:
Rewritten title (if necessary).
Rewritten message for better clarity and guidance.
Improved button labels (if applicable).

UI Component Recommendation:
Should this message be a dialog, alert, or snackbar notification?
Suggested severity level (Success, Warning, Critical, Info).

[Insert error messages here]

AI Assessment: the original message (Left) is overly technical, lacks specificity, isn’t user-friendly, and doesn’t clearly guide the user on next steps. AI Recommendations: The improved message (right) replaces technical jargon with clear, user-friendly language and provides actionable next steps.

Extracting & Structuring Dialog Messages from Code.

Task:
You are an AI assistant with expert knowledge of [programming language], helping product teams improve user-facing dialog messages. Your goal is to analyze source code, extract hardcoded dialog messages, and present them in a structured format for review by designers, technical writers, and product owners.

Analysis:
1. Review the provided [programming language] code block below and identify all hardcoded dialog messages that users would see.
2. Extract and structure the messages to make them ready for review by designers, technical writers, and product owners.
3. Categorize dialogs by their trigger, based on the logic in the code.

Output Format:
Generate a plain text file with the following structured format:

Dialog Title: [A short, meaningful name for the dialog message]
Trigger: [A brief explanation of what caused the dialog message]
Message: [The extracted dialog message]
Buttons: [Any user action options, if applicable]

[Insert code block here]

AI Assessment extracts a dialog message from Python code (left) and after further review, recommends a dialog component with a warning severity level due to potential data loss (right).

Next Up: Part 2 → A Practical Guide to Reviewing & Refining Dialog Messages with AI

In the next article, we’ll dive into the hands-on process of reviewing dialog messages with AI, using two approaches:

Plain Text (Structured Files) — Extracting messages from CSV, JSON, or localization files for AI-driven refinement.

Source Code Parsing — Using AI to identify and improve hardcoded messages in codebases.

Stay tuned!