User Manual

Explore

Feedloop AI

Context

⁠

updated : 17 July 2023

⁠

What is Context?

Supercharge your conversations with Feedloop AI Context.This cutting-edge technology seamlessly integrates contextual information into your interactions with the AI or bot, unlocking the infinite capacity to maintain and recall context over extended conversations. With deep contextual understanding, the AI delivers responses that are coherent, relevant, and seamlessly connected. Say goodbye to disjointed conversations and hello to personalized, meaningful experiences. Elevate your conversational AI to new heights with the power of infinity.Incorporate contextual information into your conversational AI agents, enhancing their understanding and improving the quality of responses.

Context Guide

Accessing Agent Settings:

Navigate to the project where you want to configure the Context feature.

Select the desired agent to configure the settings.

Access the Agent Settings page.

Adding a Context:

In the Agent Settings page, locate the "Context" tab or section.

⁠

Click on the "Add Context" button.

⁠

Choose the appropriate data type for your context (e.g., string, number, boolean, array of strings,array of object).

Provide a name for the context, which will serve as its identifier.

Add a description to provide additional information about the context.

⁠

Click the "Save" button to add the context.

To discard a context,click the "Discard" button instead of the "Save" button.

The context will be discarded without any confirmation prompt.

Managing Contexts:

You can add multiple contexts by repeating the steps mentioned in "Adding a Context".

⁠

To delete a context, locate the context you want to remove in the "Context" tab.

Click the "Close" button associated with the context.

The context will be deleted or discarded without any confirmation prompt.

Testing and Utilizing Context:

Navigate to the conversation dashboard or convo.

Initiate a conversation with your virtual assistant.

Include relevant context inputs during the conversation.

The virtual assistant will access the context data during the conversation to improve its understanding and generate more relevant responses.

To view the current context in the chat interface with the AI Agent, follow these steps:

Look for the "Current Context" button or option within the chat interface.

Click on the "Current Context" button.

A list of the current context variables will be displayed, along with the filled data obtained from the ongoing conversation.

By clicking on the "Current Context" button, you can easily access and review the context variables and their corresponding values that have been captured and utilized by the AI Agent throughout the conversation. This allows you to see how the context data influences the responses and interactions with the virtual assistant.

⁠

Best Practice

Defining Context

Identify the specific context you want to define, such as user preferences, shopping cart, or order status.

Determine the appropriate data type for the context, such as string, number, boolean, array of strings, or array of objects.

Give the context a meaningful name that reflects its purpose or content.

Provide a clear and concise description of the context, explaining its significance and how it will be used in conversations.

Understanding Context,

The context types available for configuration are:

Strings: This context type allows you to define a single value of text or characters. It can be used to capture information such as user preferences, names, addresses, or any other textual data relevant to the conversation.

Number: This context type enables you to store numerical values. It can be used for capturing quantities, prices, ratings, or any other numeric information that may be necessary during the conversation.

Boolean: This context type represents a binary value that can be either true or false. It is useful for tracking the status of certain conditions or flags, such as whether a user has agreed to terms and conditions or whether a specific action has been completed.

Array of Strings: This context type allows you to store multiple string values within an array. It is useful when you need to capture and manage a list of items, options, or categories related to the conversation.

Array of Objects: This context type allows you to store multiple objects within an array. Each object can have multiple properties, making it suitable for capturing complex data structures. It is useful when you need to store and access more detailed information or structured data during the conversation.

Context Type String

Context Name: PaymentMethod

Context Description: This context captures the user's preferred payment method, enabling seamless transactions and personalized payment options based on their chosen method.

Context Name: CustomerFeedback

Context Description: This context allows users to provide feedback or comments regarding their experience with the product, service, or overall customer journey, enabling the system to collect valuable insights and address any concerns or issues raised by the user.

Context Name: Wishlist

Context Description: This context represents the user's wishlist of desired products, allowing the system to provide personalized recommendations, price drop alerts, or notify the user when their wishlist items become available or go on sale.

Example Interaction

Context Name: CustomerFeedback

User: Hi, I'd like to share my feedback about the recent purchase I made.

AI Assistant: Of course! I'm here to listen. Please go ahead and let me know your feedback.

User: I must say, I'm really impressed with the quality of the product I received. The packaging was excellent, and the delivery was prompt. However, I did encounter a minor issue with one of the product components.

AI Assistant: Thank you for sharing your feedback. We're glad to hear that you're satisfied with most aspects of your purchase. I apologize for the inconvenience caused by the issue you encountered.

Context Type: Number

Context Name: OrderQuantity

Context Description: This context represents the quantity of a specific product that the user intends to order. It stores a numerical value indicating the desired quantity, such as 2, 5, or 10. based on the desired quantity.

Example Chat Interaction:

User: I'd like to place an order for the red t-shirt.

AI Assistant: Great! How many red t-shirts would you like to order?

User: I want to order 3 red t-shirts.