AI AutoResponse

The AI AutoResponse system uses AI to automatically respond to common user questions and support requests. Unlike traditional keyword-based systems, this AI-powered solution understands the intent and context of user messages, providing much more accurate and helpful responses.

Overview

The system analyzes every message sent in your Discord server and determines if it matches any of your predefined responses. When a match is found with sufficient confidence, the bot automatically replies with the appropriate information, helping to reduce support workload while providing instant assistance to users.

Key Benefits

• Uses OpenAI's language models to understand intent, not just keywords

• Provides immediate help to users 24/7

• Handles common questions automatically, freeing up staff time

• Detailed statistics help optimize response effectiveness

• Built-in buttons to gauge response quality and improve the system

How It Works

1. Message Analysis

When a user sends a message, the AI AutoResponse system:

1. Ensures the system is enabled and the user/channel meets criteria

2. Skips analysis for users with support roles to avoid interfering with staff responses

3. Sends the message to OpenAI for intelligent analysis

4. Compares against your configured response triggers

5. Only responds if confidence level meets the threshold

2. AI Intelligence

The system uses a sophisticated prompt that instructs the AI to:

• Understand the context and intent of messages

• Identify the user's actual need or question

• Match against available response categories

• Provide a confidence score (0.0 to 1.0)

• Explain the reasoning behind the match

3. Response Delivery

When a match is found:

• The bot replies with the configured message (text or embed format)

• Feedback buttons are added for user input

• The interaction is logged to the database

• Analytics data is updated in real-time

Configuration

Basic Settings

AIAutoResponse:
  Enabled: true # Enable/disable the entire system
  OnlyInTickets: false # Restrict to ticket channels only
  OpenAIAPIKey: "your_api_key_here" # Required OpenAI API key
  Model: "gpt-3.5-turbo" # AI model to use (gpt-3.5-turbo or gpt-4)
  ConfidenceThreshold: 0.7 # Minimum confidence to trigger response (0.0-1.0)

Response Configuration

Each response is defined with the following structure:

Responses:
  response_key_name:
    Triggers: ["keyword1", "keyword2", "phrase"] # Help AI understand what to match
    Message: "Your response message here" # What the bot will say
    Type: "EMBED" # Response format: "EMBED" or "TEXT"
    Color: "#FF5D5D" # Embed color (only for EMBED type)

Response Types

EMBED Format:

• Professional appearance with title, description, and footer

• Customizable colors and styling

• Best for important information or detailed responses

TEXT Format:

• Simple text message

• Faster and more casual

• Best for quick answers or brief information

Understanding Triggers

The Triggers array helps guide the AI's understanding but does not work like traditional keywords. Instead:

• Context Clues: Triggers help the AI understand what topics this response covers

• Intent Matching: The AI looks for the underlying intent, not exact word matches

• Flexible Matching: Users can phrase questions differently and still get matched

Example: Server Connection Response

server_connection:
  Triggers: ["server IP", "how to connect", "server address", "join server", "connection"]
  Message: "Our server IP is **play.example.com**. You can connect using any Minecraft client!"
  Type: "EMBED"
  Color: "#00FF00"

This will match messages like:

• "what's the server ip?"

• "how do i connect to your minecraft server?"

• "can't join the server, what's the address?"

• "server connection info please"

• "where do I play minecraft?"

But won't match unrelated messages like:

• "what's the weather today?"

• "how are you doing?"

• "random conversation"

Button Settings

ButtonSettings:
  Enabled: true # Show feedback buttons
  HelpfulButton: "👍 This helped!" # Positive feedback button text
  NotHelpfulButton: "👎 Still need help" # Negative feedback button text
  ButtonTimeout: 300 # How long buttons stay active (seconds)

Analytics Configuration

Statistics:
  Enabled: true # Track response statistics
  LogsChannelID: "CHANNEL_ID" # Channel for logging AI activity

Analytics:
  TrackUsage: true # Track which responses are used most
  TrackAccuracy: true # Track user feedback quality
  TrackUserSatisfaction: true # Overall satisfaction metrics
  MonthlyReports: true # Generate monthly reports

Best Practices

Writing Effective Triggers

1. Be Descriptive: Include various ways users might ask about the topic

2. Think Like Users: Consider different phrasings and terminology

3. Include Problems: Add trigger phrases for issues users might have

4. Avoid Overlap: Make sure different responses have distinct trigger contexts

Good Example:

password_reset:
  Triggers: ["password reset", "forgot password", "can't login", "account recovery", "lost password", "login issues"]

Poor Example:

password_reset:
  Triggers: ["password"] # Too vague, might match unrelated messages

Setting Confidence Thresholds

• 0.9 - 1.0: Extremely strict, only exact matches

• 0.7 - 0.9: Recommended range, good balance of accuracy and coverage

• 0.5 - 0.7: More lenient, may catch more questions but risk false positives

• Below 0.5: Too loose, likely to cause incorrect responses

Command Usage

/ai-analytics overview

View overall system performance:

• Total responses sent

• Monthly statistics

• User feedback summary

• Success rates

/ai-analytics responses

Analyze individual response performance:

• Usage frequency for each response

• Average confidence scores

• User satisfaction rates

• Most/least effective responses

/ai-analytics accuracy

Review system accuracy:

• Helpful vs not helpful feedback breakdown

• Confidence correlation with user satisfaction

• Areas needing improvement

/ai-analytics monthly [month] [year]

Generate monthly reports:

• Historical performance data

• Trend analysis

• Response effectiveness over time

Troubleshooting

Common Issues

AI Not Responding to Questions:

1. Check if ConfidenceThreshold is too high

2. Verify OpenAI API key is valid

3. Ensure user isn't staff (staff messages are ignored)

4. Check if OnlyInTickets is enabled when testing outside tickets

Too Many False Positives:

1. Increase ConfidenceThreshold value

2. Review and refine trigger keywords

3. Make response contexts more specific

4. Check for overlapping response topics

Low User Satisfaction:

1. Review response messages for clarity

2. Check if responses actually answer the questions

3. Consider adding more specific responses for common issues

4. Update outdated information in responses

The new system should be significantly more accurate and useful than the old keyword matching approach.