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Building conversational AI experiences with gen AI

October 31, 2023

A major source of frustration for any large organization, whether non-profit, public, or private, is the difficulty that individuals and teams within the organization have in locating relevant information both internally (on intranets) and externally (on the web). Employees can waste a significant amount of time trying to zero in on the right intranet or web source of information for various daily work related questions; or they might struggle to find the right internal expert to assist with the topic they seek answers about.

Google Cloud’s generative AI capabilities now enable organizations to address this pain point by leveraging Google’s best-in-class advanced conversational and search capabilities. Using Google Cloud generative AI features in Dialogflow, you can create a lifelike conversational AI agent that empowers employees to retrieve the most relevant information from internal or external knowledge bases. Generative AI features in Dialogflow leverages Large Language Models (LLMs) to power the natural-language interaction with users, and Google enterprise search to ground in the answers in the context of the knowledge bases. The knowledge base could consist of both structured and unstructured data.

In the following sections we provide an example on how to build a chat experience that handles HR benefits questions from both external websites and an internal FAQ knowledge base. Additionally, because the user may need to consult with a human HR representative for a specific situation, the Dialogflow virtual agent is able to find the right HR representative based on the conversation topic, and follow up with making an appointment by calling the calendar API. The core components of this example include:

  1. Generative AI Agent: uses generative capabilities. An agent can be used to create a lifelike chat or voice experience in minutes, for which the conversational answers would be grounded in the user-provided knowledge base.

  2. Playbooks: a Playbook is a generative agent designer for building flow and tasks to be carried out by the virtual agent. Playbooks can be designed and created simply based on instructions written in natural language.

  3. A webhook: a service that hosts business logic or calls other services, API, etc.

  4. Intents and routes: An intent categorizes an end-user intention for one conversation turn. Routes are connections among flows and conversations. We use an intent to route a conversation to the right flow.

Build a chatbot using gen AI to improve employee productivity

Getting started takes a few simple steps. First go to the Vertex AI Conversation console to build your data store/knowledge base. Then, you can start to create a transactional agent with multi-turn conversation and call external APIs using Dialogflow. Before diving into the steps, let’s look at the use case that led to creating a conversational AI experience using generative AI.

Summary

Miranda recently joined a company with 500 employees. She’s spending countless hours trying to get up to speed on understanding benefits, compensation, performance reviews, and different aspects of the organization. It would be great if Miranda had, within her company’s web portal, a lifelike virtual agent to allow employees to find the right information and right people, quickly. This use case is applicable to any industry and organization. A user journey for Miranda would go as follows:

As a new employee, Miranda wants to know:

  1. How to enroll in benefits as a new hire. For example what information she needs to provide and what internal tools she can use.

  2. What are the various benefit options available to her?

  3. How to add her dependents to a benefit, such as the dental plan.

  4. What steps she needs to update her benefits and enrollment.

Miranda also wants to consult with a HR representative in person to understand how her compensation was modeled and how her performance will impact future compensation.

Reference architecture