Chai is an innovative platform designed for two-way AI communications. Available as a smartphone app and on the web, Chai allows users to interact with AI chatbots as if they were conversing with real people. The platform offers a wide range of bots, with new ones being added regularly.

Chai is an innovative platform designed for two-way AI communications. Available as a smartphone app and on the web, Chai allows users to interact with AI chatbots as if they were conversing with real people. The platform offers a wide range of bots, with new ones being added regularly.


Chai Research is making significant strides in the field of Artificial General Intelligence (AGI) by leveraging a crowdsourcing approach to develop and refine large language models (LLMs). Unlike traditional methods, Chai harnesses the power of a community-driven model training and feedback loop, offering a unique and potentially revolutionary pathway to achieving AGI. This article explores the technology behind Chai Research, its innovative crowdsourcing methodology, and the competitive edge it holds over traditional AI development approaches.

The Concept of Crowdsourcing in AI

Crowdsourcing in AI involves distributing the development process across a wide community of contributors. At Chai, developers create small-size models, fine-tuning them on diverse datasets, which are then deployed on the Chaiverse platform. This platform allows users to interact with these models, providing valuable feedback that drives further improvements. This method supports democratization of AI development and also accelerates the refinement process by utilizing a broad spectrum of data and user interactions.

Chai Products

Chai has also developed a Python library called Chaipy, which makes it easy for developers to create, test, and deploy chatbots. The platform started gaining popularity in February 2022 and continues to grow.

The Chai app is user-friendly, and you can start a conversation with a bot on the website "" immediately. However, to get the full experience, you'll need to register. The app is available for download on both Android and iPhone devices.

To create your own bot, simply click the "Build a Bot" option in the top right of the chat page, provide the necessary information, apply changes, and start chatting.

Chai offers a free version, which allows users to send up to 100 messages per day. To get more features and unlimited messaging, users can subscribe to Chai Premium, available at $13.99 per month or $134.99 annually.

Regarding safety, Chai app has received a 4.4/5 rating on the App Store based on NLP analysis of over 21,741 user reviews. While chatbots are generally safe, it is always important to exercise caution when using them. Some users have expressed concerns about potential privacy issues, so it's essential to stay informed and vigilant while using the app.

Achieving AGI: The Path to 10 Trillion Parameters

Chai Research aims to develop a language model with approximately 10 trillion parameters, a scale deemed necessary for achieving AGI. Current models on the Chaiverse platform range from 7 billion to 13 billion parameters, each excelling in specific dimensions such as creativity, storytelling, and factual accuracy. By aggregating these models and using a sophisticated gating system, Chai can optimize performance across various user needs.

Mixture-of-Experts (MoE) Architecture

Chai employs a Mixture-of-Experts (MoE) architecture to manage and utilize its diverse models effectively. In an MoE setup, multiple small models, or "experts," are selected based on the input to generate the desired output. This approach offers several benefits:

  1. Lower Training Costs: Small models are cheaper and faster to train, making them accessible to a wider range of developers.
  2. Lower Inference Costs: Utilizing multiple small models for inference is more cost-effective than a single large model.
  3. Higher Iteration Speed: Smaller models can be iterated and improved rapidly, allowing for quick integration of user feedback.

The Chai gating model, referred to as the LLM-controller, acts as a recommender system that selects the most suitable expert model based on the conversation's context. This system optimizes the performance by aligning model capabilities with user expectations.

Competitive Edge of Chai Research

Chai's crowdsourcing model offers a distinct competitive advantage over traditional closed-source approaches:

  • Diverse Data Sets: By allowing developers to train models on varied datasets, Chai ensures a wide range of capabilities and specializations within its models.
  • Scalability: The community-driven approach facilitates rapid scaling of model parameters, aiming towards the 10 trillion mark.
  • User Engagement: Early experiments have shown a 68% increase in user engagement compared to models like GPT-3.5, highlighting the effectiveness of Chai's iterative feedback loop.

Practical Implications for AI Development

Chai Research's approach addresses several common challenges in AI development:

  • Data Scarcity: Crowdsourcing provides a constant influx of new data, essential for refining LLMs.
  • Cost Management: Lower training and inference costs make advanced AI development more accessible.
  • Customization: The ability to fine-tune models for specific tasks enhances the user experience, leading to higher engagement and satisfaction.

Key Features of Chai Research's Platform

  1. Chaiverse Platform: A hub for developers to deploy models and receive real-time user feedback.
  2. LLM-Controller: A sophisticated recommender system that selects the best-suited model for each conversation.
  3. Diverse Expert Models: Models fine-tuned on varied datasets, excelling in different dimensions.


Chai Research is pioneering a novel approach to AGI development through crowdsourcing, leveraging community-driven model training and a Mixture-of-Experts architecture. This methodology lowers costs and accelerates development and enhances model performance by utilizing diverse datasets and user feedback. As Chai continues to scale its operations, it stands poised to make significant contributions to the field of AI, potentially achieving the coveted milestone of AGI.


Chai Research employs a crowdsourcing approach to AI development, using a Mixture-of-Experts architecture to optimize small, specialized models. This method lowers costs, accelerates development, and enhances performance through diverse data and user feedback. Aiming for a 10 trillion-parameter model, Chai has shown significant user engagement improvements, positioning itself as a formidable player in the race towards AGI.

About the author
Andrew Lekashman

Andrew Lekashman

AI Product Designer, Chief Editor at Nextomoro

Exploring Possibilities with Artificial Intelligence

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