How Does an AI Assistant Work?

An AI Assistant, also known as an enterprise copilot, virtual assistant, or digital assistant, is a cutting-edge technology that leverages artificial intelligence to support employees in their work. By handling routine tasks, providing relevant information, and engaging in natural language conversations, AI Assistants streamline workflows and enhance productivity.

The technology driving AI Assistants for work is a complex integration of various components, powered by Natural Language Processing (NLP) and Large Language Models (LLMs) that enable AI Assistants to understand and respond to queries in natural human-like interactions.

Step 1: User Input

The first step in using an AI Assistant is the user input. This can be in the form of a voice command, a text-based message, or even a click within a conversational AI client. The user tells the assistant what they need help with or what task they want to complete.

Step 2: Natural Language Processing

Once the user input is received, the AI Assistant utilizes Natural Language Processing (NLP) techniques to understand and interpret the user's intent. NLP helps the assistant to analyze the input, identify the keywords, and determine the underlying meaning behind the user's request.

During the natural language processing step, there are various technologies the AI Assistant might use to help understand the user’s input. These technologies can include vector search, semantic search, keyword search, intent determination, and entity extraction to name a few.

If an AI Assistant doesn’t understand the user’s input, it has configured what is called a fallback option. Adding fallback prompts is important for handling situations where the chatbot fails to comprehend or accurately categorize a user's intention. These prompts can include asking for clarification, rephrasing the query, or providing human assistance.

Step 3: Retrieval of Information

After understanding the user's intent, the assistant searches its vast database, which contains a wealth of information and knowledge. It retrieves relevant information, facts, or data that will be useful in fulfilling the user's request or answering their query.

Depending on how the assistant is built, the knowledge can come from various sources. For example, Open AI provides pre-trained models where knowledge comes from a large amount of text data gathered from books, websites, articles, and other sources that are publicly available on the internet. Assistants using pre-trained models are versatile and are great for conversational AI, information retrieval, and summarization.

Pre-trained models do not usually contain real-time information and only contain information as far as their models have been trained. To improve accuracy, some AI Assistants will also use Retrieval Augmented Generation (RAG) which pulls up to date information from external sources before generating a response.

RAG will search and fetch relevant and real-time data and combine this new information with the assistant’s existing knowledge to improve response accuracy.

For more details about RAG, read What is Retrieval Augmented Generation (RAG)?

AI Assistants can also integrate with third-party systems via API connections to retrieve data seamlessly from multiple third-party systems. For example, Workgrid’s AI Assistant integrates across various third-party systems, enabling employees to use a single conversational interface to ask questions and perform tasks without having to jump between systems.

Domain-specific AI assistants on the other hand are designed to excel within a particular field or industry, allowing them to comprehend specific jargon, concepts, and workflows unique to that domain. For instance, in the medical field, a domain-specific AI Assistant may be trained on vast amounts of medical literature, clinical data, and patient records to assist doctors in diagnosing illnesses, recommending treatments, and staying up to date of the latest research.

Step 4: Response Generation

The final step in the process is delivering the response to the user. The AI Assistant provides the answer or completes the task requested by the user. This can be in the form of a spoken response, a text message, or even an action performed on behalf of the user. The assistant formulates a response from the varying search results using using Natural Language Generation (NLG). NLG converts the structured data into human-readable text, making it appear as if a human were providing the response. This step ensures that the assistant's reply is not only accurate but also conversational and engaging.

Depending on the type of technology used in this step, the AI Assistant will surface verbatim answers from its knowledge base or leverage RAG to summarize multiple pieces of information into one consolidated answer.

In most cases, the AI Assistant will also offer supplementary resources and cite the source of the information.

AI Assistants are transforming the way we interact with computers and get things done. By understanding user input, retrieving information, and delivering contextually relevant responses, they offer a user-friendly and efficient experience. As technology continues to advance, AI Assistants will undoubtedly play an increasingly significant role in our lives.