ChatGPT Plugins

ChatGPT plugins are tools designed to enhance or extend the capabilities of the popular natural language model. Importantly, these plugins are designed with safety as a core principle. 

Some general examples include plugins for chatbots, artificial intelligence, language translation, text analytics, entity recognition, and sentiment analysis.

How do ChatGPT plugins work?

ChatGPT plugins connect ChatGPT to various third-party applications. These plugins enable the AI to interact with developer-defined APIs, access additional models and algorithms, and enhance its own capabilities.

What does this look like, exactly? In general, ChatGPT plugins communicate with users in a more intuitive and context-aware manner such that they can:

  1. Access up-to-date information like stock prices or the latest news.
  2. Retrieve knowledge-base information. For example, personal notes or company documents, and
  3. Perform actions on the user’s behalf, such as ordering dinner or booking a rental car.

To define a plugin’s functionality, developers expose one or more API endpoints, an OpenAPI Specification, and a standardized manifest file. This allows ChatGPT to consume files and make calls to the APIs defined by the developer. 

Note that the API model can be thought of as an intelligent API caller. Provided the model is equipped with an API spec and a description of when to use the API, it can proactively call the API to perform an action.

Consider the example of a user that asks “Where can I stay in New York City for three nights?” In response, the model calls a hotel reservation plugin API, receives the API response, and devises an answer that combines the relevant data with its natural language capabilities.

Expedia’s ChatGPT plugin

To better understand how ChatGPT plugins work, let’s take a look at how travel website Expedia has simplified trip planning for users. 

Previously, ChatGPT could provide a user with general information on where to stay or what to visit, but it could not help them browse or book an airline ticket, for example.

Expedia’s plugin enables users to start a conversation on the ChatGPT website and start the process of planning a trip. In a Twitter post and video demonstration, the company demonstrated a user asking ChatGPT to help it plan a trip to Puerto Vallarta.

After the user asks if there are any direct flights from Seattle, the AI replies in the affirmatory and lists dates, airlines, real-time prices, and a link to purchase them directly on Expedia. Once that is sorted, ChatGPT provides similar details for accommodation after the user prompts it to list some family-friendly resorts.

The process can be repeated for car rentals and other travel activities. ChatGPT has also been incorporated into Expedia’s app where it remembers the conversation and saves it to a “trip” where the user can also select dates, check airline ticket prices, and so forth.

When they’re ready to book, they will be sent to the Expedia website where they can log in and see personalized options already enabled.

Future potential of ChatGPT plugins

OpenAI’s introduction of ChatGPT plugins represents a significant milestone in the development of AI chat as a way to interact with the internet. 

The release also marks the point at which ChatGPT has started to transition into a platform ecosystem where developers create plugins for the benefit of users. This transition is similar to how the App Store revolutionized the mobile industry by enabling third-party apps to be used on iPhones.

OpenAI has extended plugin access to a small number of developers and users with a ChatGPT Plus subscription. But it has plans to roll out larger-scale access over time and open up new opportunities as the system evolves and more advanced use cases become apparent.

Key takeaways

  • ChatGPT plugins are tools designed to enhance or extend the capabilities of the popular natural language model. They help ChatGPT access up-to-date information, use third-party services, and run computations. Importantly, these plugins are designed with safety as a core principle. 
  • ChatGPT plugins connect ChatGPT to various third-party applications. These plugins enable the AI to interact with developer-defined APIs, access additional models and algorithms, and enhance its own capabilities
  • The release of ChatGPT plugins heralds ChatGPT’s transition from a service into a platform ecosystem where developers create plugins for users. This evolution is similar to how the App Store and iPhone revolutionized the mobile industry.

Connected Business Model Analyses


Generalized AI consists of devices or systems that can handle all sorts of tasks on their own. The extension of generalized AI eventually led to the development of Machine learning. As an extension to AI, Machine Learning (ML) analyzes a series of computer algorithms to create a program that automates actions. Without explicitly programming actions, systems can learn and improve the overall experience. It explores large sets of data to find common patterns and formulate analytical models through learning.

Deep Learning vs. Machine Learning

Machine learning is a subset of artificial intelligence where algorithms parse data, learn from experience, and make better decisions in the future. Deep learning is a subset of machine learning where numerous algorithms are structured into layers to create artificial neural networks (ANNs). These networks can solve complex problems and allow the machine to train itself to perform a task.


DevOps refers to a series of practices performed to perform automated software development processes. It is a conjugation of the term “development” and “operations” to emphasize how functions integrate across IT teams. DevOps strategies promote seamless building, testing, and deployment of products. It aims to bridge a gap between development and operations teams to streamline the development altogether.


AIOps is the application of artificial intelligence to IT operations. It has become particularly useful for modern IT management in hybridized, distributed, and dynamic environments. AIOps has become a key operational component of modern digital-based organizations, built around software and algorithms.

Machine Learning Ops

Machine Learning Ops (MLOps) describes a suite of best practices that successfully help a business run artificial intelligence. It consists of the skills, workflows, and processes to create, run, and maintain machine learning models to help various operational processes within organizations.

OpenAI Organizational Structure

OpenAI is an artificial intelligence research laboratory that transitioned into a for-profit organization in 2019. The corporate structure is organized around two entities: OpenAI, Inc., which is a single-member Delaware LLC controlled by OpenAI non-profit, And OpenAI LP, which is a capped, for-profit organization. The OpenAI LP is governed by the board of OpenAI, Inc (the foundation), which acts as a General Partner. At the same time, Limited Partners comprise employees of the LP, some of the board members, and other investors like Reid Hoffman’s charitable foundation, Khosla Ventures, and Microsoft, the leading investor in the LP.

OpenAI Business Model

OpenAI has built the foundational layer of the AI industry. With large generative models like GPT-3 and DALL-E, OpenAI offers API access to businesses that want to develop applications on top of its foundational models while being able to plug these models into their products and customize these models with proprietary data and additional AI features. On the other hand, OpenAI also released ChatGPT, developing around a freemium model. Microsoft also commercializes opener products through its commercial partnership.


OpenAI and Microsoft partnered up from a commercial standpoint. The history of the partnership started in 2016 and consolidated in 2019, with Microsoft investing a billion dollars into the partnership. It’s now taking a leap forward, with Microsoft in talks to put $10 billion into this partnership. Microsoft, through OpenAI, is developing its Azure AI Supercomputer while enhancing its Azure Enterprise Platform and integrating OpenAI’s models into its business and consumer products (GitHub, Office, Bing).

Stability AI Business Model

Stability AI is the entity behind Stable Diffusion. Stability makes money from our AI products and from providing AI consulting services to businesses. Stability AI monetizes Stable Diffusion via DreamStudio’s APIs. While it also releases it open-source for anyone to download and use. Stability AI also makes money via enterprise services, where its core development team offers the chance to enterprise customers to service, scale, and customize Stable Diffusion or other large generative models to their needs.

Stability AI Ecosystem


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