HELP CENTER

Self-learning

Machine learning system that improves AI responses based on real conversations.

What is Auto-Learning?

Auto-Learning is a system that automatically analyzes conversations with your customers and detects patterns to improve AI responses.

The system generates improvement suggestions and tests them through A/B tests before permanently applying them to your prompt.

How it works

1

Conversation analysis

The system analyzes the interactions and measures the “similarity“ between what the AI suggests and what the agent actually sends.

2

Suggestion generation

When it detects that the agent frequently modifies the AI’s suggestions, it generates improvement suggestions with priority HIGH, MEDIUM, or LOW.

3

Automatic A/B Test

When there are 3 or more HIGH suggestions, an A/B test is created. Your current prompt (A) receives 80% of the traffic and your improved prompt (B) receives 20%.

4

Automatic decision

After 2 days and at least 50 interactions per version, the system decides which is better. If B wins, the improvements are applied permanently.

Hybrid System: Data and Behavior New

Auto-Learning now distinguishes between two types of learning: concrete data about your business and stylistic improvements in responses.

Knowledge (Data)

Specific details about your business that the AI detects during conversations and automatically saves.

  • Prices of products or services
  • Opening hours
  • Shipping or return policies
  • Addresses and contact details
  • Any factual information

The data are stored as snippets and the AI automatically uses them to respond with accurate information.

Style Improvements

Improvements to the tone, format, or behavior of responses based on your corrections.

  • More formal or informal tone
  • Use of emojis or not
  • Length of responses
  • Way of greeting or saying goodbye
  • Structure of responses

These improvements are A/B tested before being applied permanently to the prompt.

How does the Knowledge (Data) tab work?

1

Automatic detection

When you correct an answer by including a specific detail (price, schedule, etc.), the system detects it.

2

Duplicate verification

The system checks whether that data already exists or contradicts previous information.

3

Storage and synchronization

The data are saved and the AI uses them automatically in future responses without the need for A/B testing.

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Active data
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Pending review
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Marked as obsolete

Data that have not been used for more than 90 days are automatically marked as obsolete for you to review.

Smart Update New

Update multiple knowledge snippets at once using a natural language instruction. Ideal for changes that affect multiple data points, such as updating prices, changing schedules, or modifying policies.

How does it work?

1

Write your instruction

Press the “Smart Update“ button in the Knowledge tab and describe the change. For example: “We now accept PayPal and Amazon Pay“ or “Update daylight saving time to 9:00-14:00“.

2

Review the proposed changes

The AI will look for the affected knowledge snippets and show you what changes it proposes: edit, delete, or leave unchanged. You can select or deselect each change individually.

3

Apply the changes

Confirm the selected changes and they will all be applied at once. The data will automatically sync with the AI in less than 15 minutes.

How to activate Auto-Learning

  1. 1 Open the WAzion Dashboard
  2. 2 Go to the “Auto-Learning“ section
  3. 3 Activate the “Enable Auto-Learning“ switch
  4. 4 Press “Save

Having Auto-Learning enabled consumes additional credits for each conversation analyzed.

Key Performance Indicators (KPIs)

Processed Interactions

Total interactions analyzed by the system.

Average Similarity

Percentage of similarity between the AI’s suggestions and what the agent actually sends. The higher, the better the AI performs.

Applied Improvements

Total number of improvements that have passed the A/B test and have been applied to the prompt.

Next Update

Remaining interactions for the next analysis (Micro: 10, Medium: 50, Macro: 500).

Quality Metrics

Average Iterations

How many times the agent edits before sending. The goal is 1.0 (direct sending).

Perfect

Percentage of responses sent on the first attempt without modifications.

Multiple Attempts

Percentage of responses that required more than one attempt.

Pending Suggestions

Suggestions are classified by priority:

HIGH Important improvements that are tested first
MEDIUM Moderate improvements
LOW Minor improvements

When there are 3 or more accumulated HIGH suggestions, the system automatically starts an A/B test.

A/B Tests

How does the A/B system work?

1. When there are 3+ HIGH improvements, an A/B test is created automatically
2. Your current prompt (unchanged) = Version A - receives 80% of the traffic
3. Your prompt + improvements = Version B - receives 20% of the traffic
4. After 2 days and 50 interactions, the system decides which is better
5. If B wins: the improvements are applied permanently. If A wins: your prompt remains the same

Version History

Each time improvements are made, a new version of the prompt is created. From the history you can:

  • View all previous versions with date and number of improvements
  • Compare differences between versions (button “Diff“)
  • Rollback to a previous version if the improvements do not work as expected

Versions marked as “TESTING“ cannot be reverted until the A/B test is completed.

Available charts

Evolution of Similarity

Line chart showing how the average similarity has evolved over the past 30 days.

Category Distribution

Pie chart showing which categories of conversations are most frequent.

Similarity by Category

Horizontal bar chart showing the average similarity in each category. The categories with the lowest similarity are where the AI needs the most improvement.

Frequently Asked Questions

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