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what is a chatbot - Coggle Diagram

- - - - Intent Recognition Accuracy: Measure the percentage of user inputs correctly classified into the right intents.
      - Entity Recognition Accuracy: Evaluate the accuracy of identifying relevant entities within user inputs.
      - First Contact Resolution (FCR): Determine the percentage of interactions where the user's query is resolved in the first attempt without needing human intervention.
      - Fallback Rate: Track how often the chatbot fails to understand user input and provides a generic or fallback response.
  - - - Number of Interactions: Monitor the total number of interactions over a given period.
      - Active Users: Count the number of unique users interacting with the chatbot.
      - Session Length: Measure the average duration of a chatbot session.
      - Retention Rate: Assess how often users return to interact with the chatbot.
- - - - Hold-out Validation Set: Split your annotated data into a training set and a validation set. Typically, 70-80% of the data is used for training, and 20-30% is used for validation.
      - Cross-validation: Use techniques like k-fold cross-validation to ensure the model is robust and not overfitting to the training data. This involves dividing the data into k subsets, training the model k times, each time using a different subset as the validation set and the remaining as the training set.
      - Evaluation Metrics: Assess the model’s performance using metrics such as accuracy, precision, recall, and F1-score on the validation set. These metrics help determine how well the model is performing in recognizing intents and entities.
  - - - Initial Training: Train the chatbot on a large dataset.
      - Validation: Test the chatbot on a validation set to evaluate its performance.
      - Refinement: Based on validation results, refine the model (e.g., by adjusting parameters, augmenting the training data, or changing the model architecture).
      - Re-training and Re-validation: Repeat the training and validation steps until the chatbot achieves the desired level of performance.
  - - - Monitoring Performance: Regularly monitor the chatbot’s interactions with users to detect any decline in performance or new patterns in user queries.
      - Updating Training Data: Incorporate new user interactions into the training dataset to keep the chatbot up-to-date with evolving user behavior and preferences.
      - Re-training and Validation: Periodically re-train and validate the chatbot with the updated dataset to maintain and improve its performance over time.
- - - - Greeting: Initiating a conversation (e.g., "Hello", "Hi").
      - Product Inquiry: Asking about specific products or services (e.g., "What are the features of Product X?", "Tell me more about your software").
      - Pricing Inquiry: Requesting pricing information (e.g., "How much does Product X cost?", "What are your subscription plans?").
      - Order Status: Inquiring about the status of an order (e.g., "Where is my order?", "When will my delivery arrive?").
      - Technical Support: Seeking help with technical issues (e.g., "I'm having trouble logging in", "How do I reset my password?").
      - Appointment Scheduling: Requesting to schedule, reschedule, or cancel an appointment (e.g., "I’d like to book a meeting", "Cancel my appointment").
      - Feedback/Complaint: Providing feedback or lodging a complaint (e.g., "I have a complaint about your service", "Here is my feedback").
      - Account Management: Inquiries related to user accounts (e.g., "How do I update my profile?", "I want to change my password").
      - Billing and Payments: Questions related to billing and payments (e.g., "How do I pay my bill?", "I have a question about my invoice").
      - General Information: Requesting general company information (e.g., "What are your business hours?", "Where is your office located?")
  - - - Product Name: The name of the product or service (e.g., "Product X", "Software Y").
      - Date/Time: Specific dates and times (e.g., "Monday", "July 20th", "2 PM").
      - Order Number: Unique identifier for an order (e.g., "Order #12345").
      - Customer Name: The name of the customer (e.g., "John Doe").
      - Location: Geographical locations (e.g., "New York office", "Main Street branch").
      - Issue Type: The type of problem or issue (e.g., "login issue", "payment problem").
      - Payment Method: Types of payment (e.g., "credit card", "PayPal").
      - Contact Information: Phone numbers, email addresses, etc. (e.g., "johndoe@example.com", "123-456-7890")
  - - - Intent:
      - Greeting
      - Pricing Inquiry
      - Order Status
      - Entities:
      - Product Name: Product X
      - Order Number: #12345