Personalised URL links

This page provides comprehensive insights into optimizing Digital agent chatbot performance through dynamic URL manipulation. By employing these techniques, chatbots can deliver personalized experiences, thereby enhancing user engagement and satisfaction within chatbot ecosystems.

Personalized links offer numerous advantages in enhancing user experience and streamlining interactions. Some compelling use cases include:

Pre-filled search queries

Generating links with pre-filled search queries simplifies the search process for users, facilitating quicker access to relevant information.

For instance, a chatbot assisting with product recommendations could provide a personalized link to an e-commerce website with a pre-filled search query based on the user's preferences.

Pre-filled forms

Personalized links can populate form fields with user-specific information, reducing manual data entry and potential errors.

For example, a chatbot facilitating event registration could provide a personalized link to a registration form with fields pre-filled with the user's details.

Customized product or service recommendations

Personalized links can lead users to customized product or service recommendation pages tailored to their preferences, past behavior, or demographic information. This tailored browsing experience increases the likelihood of conversion.

Appointment scheduling

Personalized links can simplify appointment scheduling by directing users to booking pages with pre-filled date and time preferences. This streamlines the scheduling process and reduces friction in converting leads.

Dynamic content generation

Personalized links can dynamically generate content based on user-specific parameters, delivering highly customized and relevant experiences.

For example, a chatbot providing travel recommendations could generate personalized links to destination pages with tailored itineraries, accommodation options, and local activities based on the user's interests and budget.

The use of personalized links is primarily applicable to chat-based Digital agents, which operate within text-based interfaces, allowing for seamless integration and interaction with dynamic URLs.

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This chapter elucidates integrating personalized links within pre-set answers in chatbot interactions. By incorporating variables containing personalized data and concatenating them with the URL string, chatbots can dynamically generate personalized links tailored to individual users.

  1. Define Variables with personalized data:

    • Identify the personalized data that needs to be incorporated into the link, such as user IDs, names, or preferences.

  2. Construct Personalized Link:

    • Concatenate the string representation of the URL with the string variables containing personalized data.

  3. Use Personalized Link in Message Node:

    • Utilize the MSG node to store the constructed personalized link as part of the chatbot's response.

    • In the chatbot bubble GUI, the variable placeholder in this message will be replaced with a personalised URL. You may also use Markdown to format a clickable hyperlinked text.

Two distinct methods are elaborated upon, offering solutions tailored to scenarios where precise parameterization of URLs is pivotal.

We cannot directly add a link with variables in the knowledge base index due to the nature of the response text generated by generative AI. The response text is treated as a single string, devoid of any contextual understanding or variable recognition.

Therefore, inserting a variable placeholder within the response text would simply be interpreted as part of the string itself, rather than as a dynamic parameter to be replaced with specific values.

Method #1: Replace method


  1. Configure one AI node, one FNC node, and one MSG node.

  2. Utilize the Python replace method to generate a modified text.


  • The replace method preserves text integrity by refraining from modifying the response if the original string is absent.

  • Ensures efficient token utilization and reduced latency in comparison to incorporating an additional AI node.


  • Requires proficiency in Python programming.

  • Mandates meticulous attention during string search and replacement.

Method #2: Prompt chaining with two AI nodes


  1. Sequentially deploy two AI nodes.

  2. Configure its prompt to examine the generated answer from the previous AI node and, if a link to be personalised is detected, define the format of a link meant to replace it.


  • Eliminates the necessity for programming skills; instructions can be formulated using natural language.

  • Exhibits tolerance towards input variations, accommodating semantic nuances.


  • Offers limited control over the decision-making process of generative AI.

  • Possibility of incomplete or unexpected response modifications.

  • The involvement of two AI nodes leads to heightened token consumption and latency in providing answers.


  • Data Prerequisite:

    • Both methods outlined in this documentation rely on the availability of personalized data, such as email addresses, IDs, or other user-specific information.

    • These personalized data might be obtained from users during previous steps of the conversation or from a third source via APIs. example: personalised_email is ""

    • For the Python replace method, the personalized link must be constructed as a concatenation of strings or string variables.

  • Understanding Target Page Structure:

    • Knowledge of the structure of the target page is crucial for successful implementation. This understanding enables the incorporation of pre-filled values into the URL, such as pre-filled search fields or form items. example:

  • Desired Functionality Upon Clicking Modified Hyperlink:

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