OpenAI GPT-3.5 AI technology Top Builders

Explore the top contributors showcasing the highest number of OpenAI GPT-3.5 AI technology app submissions within our community.

OpenAI GPT-3.5

GPT-3.5 is a set of models that improve on GPT-3 and can understand as well as generate natural language or code. It is an autoregressive language model (LLM) from OpenAI that uses deep learning to produce human-like text. It is a fine-tuned version of GPT-3, the third-generation language prediction model in the GPT series created by OpenAI. GPT-3.5 has garnered significant attention and acclaim for its unparalleled ability to understand and generate human-like text. With an astounding 175 billion parameters at its disposal, GPT-3.5 stands as one of the most expansive and powerful language models ever constructed at the time of its release.

GPT-3.5's exceptional performance is not only limited to its sheer size but also stems from its highly refined architecture. Harnessing the power of deep learning, GPT-3.5 delivers consistently accurate and relevant results, elevating the standard for language models and establishing itself as a trailblazer in the field of artificial intelligence.

Relese dateMarch 15, 2022
TypeAutoregressive, Transformer, Language model

Start building with OpenAI GPT-3.5

OpenAI GPT-3 has a rich ecosystem of libraries and resources to help you get started. We have collected the best GPT-3.5 libraries and resources to help you get started to build with GPT-3 today. To see what others are building with GPT-3, check out the community built GPT-3 Use Cases and Applications.

OpenAI GPT-3.5 Tutorials

OpenAI GPT-3.5 Boilerplates

Kickstart your development with a GPT-3.5 based boilerplate. Boilerplates is a great way to headstart when building your next project with GPT-3.

OpenAI GPT-3.5 Libraries

A curated list of libraries and technologies to help you build great projects with GPT-3.5.

OpenAI GPT-3.5 AI technology Hackathon projects

Discover innovative solutions crafted with OpenAI GPT-3.5 AI technology, developed by our community members during our engaging hackathons.



SecureSpeak Enterprise emerges as a cutting-edge solution in the realm of secure digital communication, catering to the pressing need for privacy and data integrity in business interactions. At its foundation lies a sophisticated self-deployed language model, designed to scrutinize user inputs instantaneously, censoring any sensitive information with a blend of pre-configured rules and insights gleaned from historical data. This ensures that every piece of information is treated with the utmost confidentiality right from the start. SecureSpeak employs an innovative dual-storage system. This system archives both the original and the censored versions of inputs within SQL and vector databases, facilitating not only robust data management but also seamless retrieval and analysis. This strategic approach to data storage preserves the context and meaning of information, all while upholding stringent confidentiality standards. Central to SecureSpeak’s functionality is its use of Retrieval-Augmented Generation (RAG), powered by Vectara. This mechanism enriches the platform's responses with semantically related content from an extensive corporate database, alongside the capability to perform on-demand, context-specific queries. This not only enhances the relevance and accuracy of the responses but also ensures they remain within the bounds of privacy regulations. The SecureSpeak journey extends beyond immediate data processing to include the active refinement and application of collected insights. This process serves to enhance the overall user experience significantly, turning raw data into a strategic asset. Additionally, the system's language model is subject to ongoing fine-tuning, learning continuously from processed data to elevate its performance in censoring and generating responses. Through these meticulously designed features and processes, SecureSpeak Enterprise sets a new standard in secure, intelligent, and privacy-conscious digital communication.